Above are papers related to the following paper: [Paper_ID] Paper #3218. 	[Title] Rule Induction with CN2: Some Recent Improvements [Abstract] The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan's C4.5 are also made. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #235776. 	[Title] Recursive Automatic Algorithm Selection for Inductive Learning [Abstract] COINS Technical Report 94-61 August 1994 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17798. 	[Title] Using Decision Trees to Improve Case-Based Learning [Abstract] This paper shows that decision trees can be used to improve the performance of case-based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-flexible prediction that lies between the classification task performed by decision tree algorithms and the flexible prediction task performed by conceptual clustering systems. In semi-flexible prediction, learning should improve prediction of a specific set of features known a priori rather than a single known feature (as in classification) or an arbitrary set of features (as in conceptual clustering). We describe one such task from natural language processing and present experiments that compare solutions to the problem using decision trees, CBL, and a hybrid approach that combines the two. In the hybrid approach, decision trees are used to specify the features to be included in k-nearest neighbor case retrieval. Results from the experiments show that the hybrid approach outperforms both the decision tree and case-based approaches as well as two case-based systems that incorporate expert knowledge into their case retrieval algorithms. Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2440. 	[Title] Quinlan, 1990 J.R. Quinlan. Learning logical definitions from relations. Machine Learning, First-order theory revision. In [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6923. 	[Title] Inductive Constraint Logic [Abstract] A novel approach to learning first order logic formulae from positive and negative examples is presented. Whereas present inductive logic programming systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or false for the target theory. This viewpoint allows to reconcile the inductive logic programming paradigm with classical attribute value learning in the sense that the latter is a special case of the former. Because of this property, we are able to adapt AQ and CN2 type algorithms in order to enable learning of full first order formulae. However, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form, we will use a clausal representation, which corresponds to a conjuctive normal form where each conjunct forms a constraint on positive examples. This representation duality reverses also the role of positive and negative examples, both in the heuristics and in the algorithm. The resulting theory is incorporated in a system named ICL (Inductive Constraint Logic). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6925. 	[Title] Solving the Multiple-Instance Problem with Axis-Parallel Rectangles [Abstract] The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple-instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk-odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #277263. 	[Title] Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries [Abstract] Machine learning techniques can be used to extract knowledge from data stored in medical databases. In our application, various machine learning algorithms were used to extract diagnostic knowledge to support the diagnosis of sport injuries. The applied methods include variants of the Assistant algorithm for top-down induction of decision trees, and variants of the Bayesian classifier. The available dataset was insufficent for reliable diagnosis of all sport injuries considered by the system. Consequently, expert-defined diagnostic rules were added and used as pre-classifiers or as generators of additional training instances for injuries with few training examples. Experimental results show that the classification accuracy and the explanation capability of the naive Bayesian classifier with the fuzzy discretization of numerical attributes was superior to other methods and was estimated as the most appro priate for practical use. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69397. 	[Title] Dynamical Selection of Learning Algorithms [Abstract] Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain have met with limited success. This paper proposes a new approach to predicting a given example's class by locating it in the "example space" and then choosing the best learner(s) in that region of the example space to make predictions. The regions of the example space are defined by the prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region. This dynamic approach to learning algorithm selection is compared to other methods for selecting from multiple learning algorithms. The approach is then extended to weight rather than select the algorithms according to their past performance in a given region. Both approaches are further evaluated on a set of Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain (e.g. [Aha92, Breiman84]) or for a portion of the domain ([Brodley93, Brodley94]) have met with limited success. This paper proposes a new approach that dynamically selects a learning algorithm for each example by locating it in the "example space" and then choosing the best learner(s) for prediction in that part of the example space. The regions of the example space are formed by the observed prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region which is defined by the "cross-validation history." This paper introduces DS, a method for the dynamic selection of a learning algorithm(s). We call it "dynamic" because the learning algorithm(s) used to classify a novel example depends on that example. Preliminary experimentation motivated DW, an extension to DS that dynamically weights the learners predictions according to their regional accuracy. Further experimentation compares DS and DW to a collection of other meta-learning strategies such as cross-validation ([Breiman84]) and various forms of stacking ([Wolpert92]). In this phase of the experiementation, the meta-learners have six constituent learners which are heterogeneous in their search and representation methods (e.g. a rule learner, CN2 [Clark89]; a decision tree learner, C4.5 [Quinlan93]; an oblique decision tree learner, OC1 [Murthy93]; an instance-based learner, PEBLS [Cost93]; a k-nearest neighbor learner, ten domains and compared to several other meta-learning strategies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110426. 	[Title] Incremental Reduced Error Pruning [Abstract] This paper outlines some problems that may occur with Reduced Error Pruning in relational learning algorithms, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of the algorithm cannot be recommended for domains which require a very specific concept description. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46491. 	[Title] ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees [Abstract] We discuss an approach to constructing composite features during the induction of decision trees. The composite features correspond to m-of-n concepts. There are three goals of this research. First, we explore a family of greedy methods for building m-of-n concepts (one of which, GS, is described in this paper). Second, we show how these concepts can be formed as internal nodes of decision trees, serving as a bias to the learner. Finally, we evaluate the method on several artificially generated and naturally occurring data sets to determine the effects of this bias. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6941. 	[Title] Multi-class problems and discretization in ICL Extended abstract [Abstract] Handling multi-class problems and real numbers is important in practical applications of machine learning to KDD problems. While attribute-value learners address these problems as a rule, very few ILP systems do so. The few ILP systems that handle real numbers mostly do so by trying out all real values that are applicable, thus running into efficiency or overfitting problems. This paper discusses some recent extensions of ICL that address these problems. ICL, which stands for Inductive Constraint Logic, is an ILP system that learns first order logic formulae from positive and negative examples. The main charateristic of ICL is its view on examples. These are seen as interpretations which are true or false for the clausal target theory (in CNF). We first argue that ICL can be used for learning a theory in a disjunctive normal form (DNF). With this in mind, a possible solution for handling more than two classes is given (based on some ideas from CN2). Finally, we show how to tackle problems with continuous values by adapting discretization techniques from attribute value learners. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22431. 	[Title] An investigation of noise-tolerant relational concept learning algorithms [Abstract] We discuss the types of noise that may occur in relational learning systems and describe two approaches to addressing noise in a relational concept learning algorithm. We then evaluate each approach experimentally. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8224. 	[Title] Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine [Abstract] Ourston and Mooney, 1990b ] D. Ourston and R. J. Mooney. Improving shared rules in multiple category domain theories. Technical Report AI90-150, Artificial Intelligence Labora tory, University of Texas, Austin, TX, December 1990. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #178209. 	[Title] SFOIL: Stochastic Approach to Inductive Logic Programming [Abstract] Current systems in the field of Inductive Logic Programming (ILP) use, primarily for the sake of efficiency, heuristically guided search techniques. Such greedy algorithms suffer from local optimization problem. Present paper describes a system named SFOIL, that tries to alleviate this problem by using a stochastic search method, based on a generalization of simulated annealing, called Markovian neural network. Various tests were performed on benchmark, and real-world domains. The results show both, advantages and weaknesses of stochastic approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152290. 	[Title] Unsupervised Constructive Learning [Abstract] In constructive induction (CI), the learner's problem representation is modified as a normal part of the learning process. This is useful when the initial representation is inadequate or inappropriate. In this paper, I argue that the distinction between constructive and non-constructive methods is unclear. I propose a theoretical model which allows (a) a clean distinction to be made and (b) the process of CI to be properly motivated. I also show that although constructive induction has been used almost exclusively in the context of supervised learning, there is no reason why it cannot form a part of an unsupervised regime. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22563. 	[Title] A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping [Abstract] The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially. A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120169. 	[Title] What do Constructive Learners Really Learn? [Abstract] In constructive induction (CI), the learner's problem representation is modified as a normal part of the learning process. This may be necessary if the initial representation is inadequate or inappropriate. However, the distinction between constructive and non-constructive methods appears to be highly ambiguous. Several conventional definitions of the process of constructive induction appear to include all conceivable learning processes. In this paper I argue that the process of constructive learning should be identified with that of relational learning (i.e., I suggest that Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #591017. 	[Title] Producing More Comprehensible Models While Retaining Their Performance [Abstract] Rissanen's Minimum Description Length (MDL) principle is adapted to handle continuous attributes in the Inductive Logic Programming setting. Application of the developed coding as a MDL pruning mechanism is devised. The behavior of the MDL pruning is tested in a synthetic domain with artificially added noise of different levels and in two real life problems | modelling of the surface roughness of a grinding workpiece and modelling of the mutagenicity of nitroaromatic compounds. Results indicate that MDL pruning is a successful parameter-free noise fighting tool in real-life domains since it acts as a safeguard against building too complex models while retaining the accuracy of the model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127851. 	[Title] Generating Declarative Language Bias for Top-Down ILP Algorithms [Abstract] Many of today's algorithms for Inductive Logic Programming (ILP) put a heavy burden and responsibility on the user, because their declarative bias have to be defined in a rather low-level fashion. To address this issue, we developed a method for generating declarative language bias for top-down ILP systems from high-level declarations. The key feature of our approach is the distinction between a user level and an expert level of language bias declarations. The expert provides abstract meta-declarations, and the user declares the relationship between the meta-level and the given database to obtain a low-level declarative language bias. The suggested languages allow for compact and abstract specifications of the declarative language bias for top-down ILP systems using schemata. We verified several properties of the translation algorithm that generates schemata, and applied it successfully to a few chemical domains. As a consequence, we propose to use a two-level approach to generate declarative language bias. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10796. 	[Title] An Improved Algorithm for Incremental Induction of Decision Trees [Abstract] Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called `slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49843. 	[Title] Fossil: A Robust Relational Learner [Abstract] The research reported in this paper describes Fossil, an ILP system that uses a search heuristic based on statistical correlation. This algorithm implements a new method for learning useful concepts in the presence of noise. In contrast to Foil's stopping criterion, which allows theories to grow in complexity as the size of the training sets increases, we propose a new stopping criterion that is independent of the number of training examples. Instead, Fossil's stopping criterion depends on a search heuristic that estimates the utility of literals on a uniform scale. In addition we outline how this feature can be used for top-down pruning and present some preliminary results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49844. 	[Title] A Comparison of Pruning Methods for Relational Concept Learning [Abstract] Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are more accurate, but much slower, because they have to generate an overly specific concept description first. We have experimented with a variety of pruning methods, including two new methods that try to combine and integrate pre- and post-pruning in order to achieve both accuracy and efficiency. This is verified with test series in a chess position classification task. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #84020. 	[Title] Classifiers: A Theoretical and Empirical Study [Abstract] This paper describes how a competitive tree learning algorithm can be derived from first principles. The algorithm approximates the Bayesian decision theoretic solution to the learning task. Comparative experiments with the algorithm and the several mature AI and statistical families of tree learning algorithms currently in use show the derived Bayesian algorithm is consistently as good or better, although sometimes at computational cost. Using the same strategy, we can design algorithms for many other supervised and model learning tasks given just a probabilistic representation for the kind of knowledge to be learned. As an illustration, a second learning algorithm is derived for learning Bayesian networks from data. Implications to incremental learning and the use of multiple models are also discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49847. 	[Title] Top-Down Pruning in Relational Learning [Abstract] Pruning is an effective method for dealing with noise in Machine Learning. Recently pruning algorithms, in particular Reduced Error Pruning, have also attracted interest in the field of Inductive Logic Programming. However, it has been shown that these methods can be very inefficient, because most of the time is wasted for generating clauses that explain noisy examples and subsequently pruning these clauses. We introduce a new method which searches for good theories in a top-down fashion to get a better starting point for the pruning algorithm. Experiments show that this approach can significantly lower the complexity of the task without losing predictive accuracy. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106103. 	[Title] Bias and the Probability of Generalization [Abstract] In order to be useful, a learning algorithm must be able to generalize well when faced with inputs not previously presented to the system. A bias is necessary for any generalization, and as shown by several researchers in recent years, no bias can lead to strictly better generalization than any other when summed over all possible functions or applications. This paper provides examples to illustrate this fact, but also explains how a bias or learning algorithm can be better than another in practice when the probability of the occurrence of functions is taken into account. It shows how domain knowledge and an understanding of the conditions under which each learning algorithm performs well can be used to increase the probability of accurate generalization, and identifies several of the conditions that should be considered when attempting to select an appropriate bias for a particular problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106492. 	[Title] Stochastically Guided Disjunctive Version Space Learning [Abstract] This paper presents an incremental concept learning approach to identiflcation of concepts with high overall accuracy. The main idea is to address the concept overlap as a central problem when learning multiple descriptions. Many traditional inductive algorithms, as those from the disjunctive version space family considered here, face this problem. The approach focuses on combinations of confldent, possibly overlapping, concepts with an original stochastic complexity formula. The focusing is e-cient because it is organized as a simulated annealing-based beam search. The experiments show that the approach is especially suitable for developing incremental learning algorithms with the following advantages: flrst, it generates highly accurate concepts; second, it overcomes to a certain degree the sensitivity to the order of examples; and third, it handles noisy examples. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120444. 	[Title] Systematic Evaluation of Design Decisions in CBR Systems [Abstract] Two important goals in the evaluation of an AI theory or model are to assess the merit of the design decisions in the performance of an implemented computer system and to analyze the impact in the performance when the system faces problem domains with different characteristics. This is particularly difficult in case-based reasoning systems because such systems are typically very complex, as are the tasks and domains in which they operate. We present a methodology for the evaluation of case-based reasoning systems through systematic empirical experimentation over a range of system configurations and environmental conditions, coupled with rigorous statistical analysis of the results of the experiments. This methodology enables us to understand the behavior of the system in terms of the theory and design of the computational model, to select the best system configuration for a given domain, and to predict how the system will behave in response to changing domain and problem characteristics. A case study of a mul-tistrategy case-based and reinforcement learning system which performs autonomous robotic navigation is presented as an example. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6334. 	[Title] Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms [Abstract] With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term prototypes refers to the reference instances used in a nearest neighbor computation the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely on stochastic techniques to search the space of sets of prototypes and are simple to implement. The first is a Monte Carlo sampling algorithm; the second applies random mutation hill climbing. On four datasets we show that only three or four prototypes sufficed to give predictive accuracy equal or superior to a basic nearest neighbor algorithm whose run-time storage costs were approximately 10 to 200 times greater. We briefly investigate how random mutation hill climbing may be applied to select features and prototypes simultaneously. Finally, we explain the performance of the sampling algorithm on these datasets in terms of a statistical measure of the extent of clustering displayed by the target classes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35070. 	[Title] The RISE System: Conquering Without Separating [Abstract] Current rule induction systems (e.g. CN2) typically rely on a "separate and conquer" strategy, learning each rule only from still-uncovered examples. This results in a dwindling number of examples being available for learning successive rules, adversely affecting the system's accuracy. An alternative is to learn all rules simultaneously, using the entire training set for each. This approach is implemented in the Rise 1.0 system. Empirical comparison of Rise with CN2 suggests that "conquering without separating" performs similarly to its counterpart in simple domains, but achieves increasingly substantial gains in accuracy as the domain difficulty grows. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #14529. 	[Title] Concept Learning and Flexible Weighting [Abstract] We previously introduced an exemplar model, named GCM-ISW, that exploits a highly flexible weighting scheme. Our simulations showed that it records faster learning rates and higher asymptotic accuracies on several artificial categorization tasks than models with more limited abilities to warp input spaces. This paper extends our previous work; it describes experimental results that suggest human subjects also invoke such highly flexible schemes. In particular, our model provides significantly better fits than models with less flexibility, and we hypothesize that humans selectively weight attributes depending on an item's location in the input space. We need more flexible models Many theories of human concept learning posit that concepts are represented by prototypes (Reed, 1972) or exemplars (Medin & Schaffer, 1978). Prototype models represent concepts by the "best example" or "central tendency" of the concept. 1 A new item belongs in a category C if it is relatively similar to C's prototype. Prototype models are relatively inflexible; they discard a great deal of information that people use during concept learning (e.g., the number of exemplars in a concept (Homa & Cultice, 1984), the variability of features (Fried & Holyoak, 1984), correlations between features (Medin et al., 1982), and the particular exemplars used (Whittlesea, 1987)). of concept learning Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #11335. 	[Title] Concept Learning and the Problem of Small [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85449. 	[Title] A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks [Abstract] With most machine learning methods, if the given knowledge representation space is inadequate then the learning process will fail. This is also true with methods using neural networks as the form of the representation space. To overcome this limitation, an automatic construction method for a neural network is proposed. This paper describes the BP-HCI method for a hypothesis-driven constructive induction in a neural network trained by the backpropagation algorithm. The method searches for a better representation space by analyzing the hypotheses generated in each step of an iterative learning process. The method was applied to ten problems, which include, in particular, exclusive-or, MONK2, parity-6BIT and inverse parity-6BIT problems. All problems were successfully solved with the same initial set of parameters; the extension of representation space was no more than necessary extension for each problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85452. 	[Title] Constructive Induction Using a Non-Greedy Strategy for Feature Selection [Abstract] We present a method for feature construction and selection that finds a minimal set of conjunctive features that are appropriate to perform the classification task. For problems where this bias is appropriate, the method outperforms other constructive induction algorithms and is able to achieve higher classification accuracy. The application of the method in the search for minimal multi-level boolean expressions is presented and analyzed with the help of some examples. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106388. 	[Title] Predicting Ordinal Classes in ILP [Abstract] This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes in an ILP setting. We start with a relational regression algorithm named SRT (Structural Regression Trees) and study various ways of transforming it into a first-order learner for ordinal classification tasks. Combinations of these algorithm variants with several data preprocessing methods are compared on two ILP benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression in relational learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6741. 	[Title] LEARNING CONCEPTS BY ASKING QUESTIONS [Abstract] Tw o important issues in machine learning are explored: the role that memory plays in acquiring new concepts; and the extent to which the learner can take an active part in acquiring these concepts. This chapter describes a program, called Marvin, which uses concepts it has learned previously to learn new concepts. The program forms hypotheses about the concept being learned and tests the hypotheses by asking the trainer questions. Learning begins when the trainer shows Marvin an example of the concept to be learned. The program determines which objects in the example belong to concepts stored in the memory. A description of the new concept is formed by using the information obtained from the memory to generalize the description of the training example. The generalized description is tested when the program constructs new examples and shows these to the trainer, asking if they belong to the target concept. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39127. 	[Title] Estimating Attributes: Analysis and Extensions of RELIEF [Abstract] In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #187354. 	[Title] Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra [Abstract] We present a novel application of ILP to the problem of diterpene structure elucidation from 13 C NMR spectra. Diterpenes are organic compounds of low molecular weight that are based on a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because of their use as lead compounds in the search for new pharmaceutical effectors. The structure elucidation of diterpenes based on 13 C NMR spectra is usually done manually by human experts with specialized background knowledge on peak patterns and chemical structures. In the process, each of the 20 skeletal atoms is assigned an atom number that corresponds to its proper place in the skeleton and the diterpene is classified into one of the possible skeleton types. We address the problem of learning classification rules from a database of peak patterns for diterpenes with known structure. Recently, propositional learning was successfully applied to learn classification rules from spectra with assigned atom numbers. As the assignment of atom numbers is a difficult process in itself (and possibly indistinguishable from the classification process), we apply ILP, i.e., relational learning, to the problem of classifying spectra without assigned atom numbers. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1117786. 	[Title] An intelligent search method using Inductive Logic Programming [Abstract] We propose a method to use Inductive Logic Programming to give heuristic functions for searching goals to solve problems. The method takes solutions of a problem or a history of search and a set of background knowledge on the problem. In a large class of problems, a problem is described as a set of states and a set of operators, and is solved by finding a series of operators. A solution, a series of operators that brings an initial state to a final state, is transformed into positive and negative examples of a relation "better-choice", which describes that an operator is better than others in a state. We also give a way to use the "better-choice" relation as a heuristic function. The method can use any logic program as background knowledge to induce heuristics, and induced heuristics has high readability. The paper inspects the method by applying to a puzzle. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #66782. 	[Title] Using Qualitative Models to Guide Inductive Learning [Abstract] This paper presents a method for using qualitative models to guide inductive learning. Our objectives are to induce rules which are not only accurate but also explainable with respect to the qualitative model, and to reduce learning time by exploiting domain knowledge in the learning process. Such ex-plainability is essential both for practical application of inductive technology, and for integrating the results of learning back into an existing knowledge-base. We apply this method to two process control problems, a water tank network and an ore grinding process used in the mining industry. Surprisingly, in addition to achieving explainability the classificational accuracy of the induced rules is also increased. We show how the value of the qualitative models can be quantified in terms of their equivalence to additional training examples, and finally discuss possible extensions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105505. 	[Title] A Divide-and-Conquer Approach to Learning from Prior Knowledge [Abstract] This paper introduces a new machine learning task|model calibration|and presents a method for solving a particularly difficult model calibration task that arose as part of a global climate change research project. The model calibration task is the problem of training the free parameters of a scientific model in order to optimize the accuracy of the model for making future predictions. It is a form of supervised learning from examples in the presence of prior knowledge. An obvious approach to solving calibration problems is to formulate them as global optimization problems in which the goal is to find values for the free parameters that minimize the error of the model on training data. Unfortunately, this global optimization approach becomes computationally infeasible when the model is highly nonlinear. This paper presents a new divide-and-conquer method that analyzes the model to identify a series of smaller optimization problems whose sequential solution solves the global calibration problem. This paper argues that methods of this kind|rather than global optimization techniques|will be required in order for agents with large amounts of prior knowledge to learn efficiently. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #93923. 	[Title] Inductive Constraint Logic and the Mutagenesis Problem [Abstract] A novel approach to learning first order logic formulae from positive and negative examples is incorporated in a system named ICL (Inductive Constraint Logic). In ICL, examples are viewed as interpretations which are true or false for the target theory, whereas in present inductive logic programming systems, examples are true and false ground facts (or clauses). Furthermore, ICL uses a clausal representation, which corresponds to a conjunctive normal form where each conjunct forms a constraint on positive examples, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form. We present some experiments with this new system on the mutagenesis problem. These experiments illustrate some of the differences with other systems, and indicate that our approach should work at least as well as the more classical approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27241. 	[Title] Error Reduction through Learning Multiple Descriptions [Abstract] Learning multiple descriptions for each class in the data has been shown to reduce generalization error but the amount of error reduction varies greatly from domain to domain. This paper presents a novel empirical analysis that helps to understand this variation. Our hypothesis is that the amount of error reduction is linked to the "degree to which the descriptions for a class make errors in a correlated manner." We present a precise and novel definition for this notion and use twenty-nine data sets to show that the amount of observed error reduction is negatively correlated with the degree to which the descriptions make errors in a correlated manner. We empirically show that it is possible to learn descriptions that make less correlated errors in domains in which many ties in the search evaluation measure (e.g. information gain) are experienced during learning. The paper also presents results that help to understand when and why multiple descriptions are a help (irrelevant attributes) and when they are not as much help (large amounts of class noise). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #568045. 	[Title] An application of ILP in a musical database: learning to compose the two-voice counterpoint [Abstract] We describe SFOIL, a descendant of FOIL that uses the advanced stochastic search heuristic, and its application in learning to compose the two-voice counterpoint. The application required learning a 4-ary relation from more than 20.000 training instances. SFOIL is able to efficiently deal with this learning task which is to our knowledge one of the most complex learning task solved by an ILP system. This demonstrates that ILP systems can scale up to real databases and that top-down ILP systems that use the covering approach and advanced search strategies are appropriate for knowledge discovery in databases and are promising for further investigation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6767. 	[Title] Generalizing from Case Studies: A Case Study [Abstract] Most empirical evaluations of machine learning algorithms are case studies evaluations of multiple algorithms on multiple databases. Authors of case studies implicitly or explicitly hypothesize that the pattern of their results, which often suggests that one algorithm performs significantly better than others, is not limited to the small number of databases investigated, but instead holds for some general class of learning problems. However, these hypotheses are rarely supported with additional evidence, which leaves them suspect. This paper describes an empirical method for generalizing results from case studies and an example application. This method yields rules describing when some algorithms significantly outperform others on some dependent measures. Advantages for generalizing from case studies and limitations of this particular approach are also described. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114864. 	[Title] Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1) [Abstract] This is a brief annotated bibliography that I wanted to make available to the attendees of my Machine Learning tutorial at the 1995 AI & Statistics Workshop. These slides are available in my WWW pages under slides. Please contact me if you have any questions. Please also note the date (listed above) on which this was most recently updated. While I plan to make occasional updates to this file, it is bound to be outdated quickly. Also, I apologize for the lack of figures, but my time on this project is limited and the slides should compensate. Finally, this bibliography is, by definition, This book is now out of date. Both Pat Langley and Tom Mitchell are in the process of writing textbooks on this subject, but we're still waiting for them. Until then, I suggest looking at both the Readings and the recent ML conference proceedings (both International and European). There are also a few introductory papers on this subject, though I haven't gotten around to putting them in here yet. However, Pat Langley and Dennis Kibler (1988) have written a good paper on ML as an empirical science, and Pat has written several editorials of use to the ML author (Langley 1986; 1987; 1990). incomplete, and I've left out many other references that may be of some use. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #75121. 	[Title] The Utility of Knowledge in Inductive Learning  Running Head: Knowledge in Inductive Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116081. 	[Title] Induction of decision trees using RELIEFF [Abstract] In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies between them. Greedy search prevents current inductive machine learning algorithms to detect significant dependencies between the attributes. Recently, Kira and Rendell developed the RELIEF algorithm for estimating the quality of attributes that is able to detect dependencies between attributes. We show strong relation between RELIEF's estimates and impurity functions, that are usually used for heuristic guidance of inductive learning algorithms. We propose to use RELIEFF, an extended version of RELIEF, instead of myopic impurity functions. We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step. The algorithm is tested on several artificial and several real world problems. Results show the advantage of the presented approach to inductive learning and open a wide rang of possibilities for using RELIEFF. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119987. 	[Title] Case-Based Sonogram Classification [Abstract] This report replicates and extends results reported by Naval Air Warfare Center (NAWC) personnel on the automatic classification of sonar images. They used novel case-based reasoning systems in their empirical studies, but did not obtain comparative analyses using standard classification algorithms. Therefore, the quality of the NAWC results were unknown. We replicated the NAWC studies and also tested several other classifiers (i.e., both case-based and otherwise) from the machine learning literature. These comparisons and their ramifications are detailed in this paper. Next, we investigated Fala and Walker's two suggestions for future work (i.e., on combining their similarity functions and on an alternative case representation). Finally, we describe several ways to incorporate additional domain-specific knowledge when applying case-based classifiers to similar tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #44017. 	[Title] Learning Decision Lists Using Homogeneous Rules [Abstract] A decision list is an ordered list of conjunctive rules (?). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem | the classification accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in concert. Existing algorithms solve this problem by adopting a greedy, iterative structure. Once a rule is learned, the training examples that match the rule are removed from the training set. We propose a novel solution to the problem: composing decision lists from homogeneous rules, rules whose classification accuracy does not change with their position in the decision list. We prove that the problem of finding a maximally accurate decision list can be reduced to the problem of finding maximally accurate homogeneous rules. We report on the performance of our algorithm on data sets from the UCI repository and on the MONK's problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6639. 	[Title] Stochastic search in inductive concept learning [Abstract] Concept learning can be viewed as search of the space of concept descriptions. The hypothesis language determines the search space. In standard inductive learning algorithms, the structure of the search space is determined by generalization/specialization operators. Algorithms perform locally optimal search by using a hill-climbing and/or a beam-search strategy. To overcome this limitation, concept learning can be viewed as stochastic search of the space of concept descriptions. The proposed stochastic search method is based on simulated annealing which is known as a successful means for solving combinatorial optimization problems. The stochastic search method, implemented in a rule learning system ATRIS, is based on a compact and efficient representation of the problem and the appropriate operators for structuring the search space. Furthermore, by heuristic pruning of the search space, the method enables also handling of imperfect data. The paper introduces the stochastic search method, describes the ATRIS learning algorithm and gives results of the experiments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153264. 	[Title]  [Abstract] Design and Evaluation of the RISE 1.0 Learning System Pedro Domingos pedrod@ics.uci.edu Technical Report 94-34 August 30, 1994 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116084. 	[Title] Linear Space Induction in First Order Logic with RELIEFF [Abstract] Current ILP algorithms typically use variants and extensions of the greedy search. This prevents them to detect significant relationships between the training objects. Instead of myopic impurity functions, we propose the use of the heuristic based on RELIEF for guidance of ILP algorithms. At each step, in our ILP-R system, this heuristic is used to determine a beam of candidate literals. The beam is then used in an exhaustive search for a potentially good conjunction of literals. From the efficiency point of view we introduce interesting declarative bias which enables us to keep the growth of the training set, when introducing new variables, within linear bounds (linear with respect to the clause length). This bias prohibits cross-referencing of variables in variable dependency tree. The resulting system has been tested on various artificial problems. The advantages and deficiencies of our approach are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #417017. 	[Title] Relating Relational Learning Algorithms [Abstract] Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51834. 	[Title] Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach [Abstract] Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and other theory-guided systems face. First, a representation language appropriate for the initial theory may be inappropriate for an improved theory. While the original representation may concisely express the initial theory, a more accurate theory forced to use that same representation may be bulky, cumbersome, and difficult to reach. Second, a theory structure suitable for a coarse domain theory may be insufficient for a fine-tuned theory. Systems that produce only small, local changes to a theory have limited value for accomplishing complex structural alterations that may be required. Consequently, advanced theory-guided learning systems require flexible representation and flexible structure. An analysis of various theory revision systems and theory-guided learning systems reveals specific strengths and weaknesses in terms of these two desired properties. Designed to capture the underlying qualities of each system, a new system uses theory-guided constructive induction. Experiments in three domains show improvement over previous theory-guided systems. This leads to a study of the behavior, limitations, and potential of theory-guided constructive induction. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #372862. 	[Title] Lookahead and Discretization in ILP [Abstract] We present and evaluate two methods for improving the performance of ILP systems. One of them is discretization of numerical attributes, based on Fayyad and Irani's text [9], but adapted and extended in such a way that it can cope with some aspects of discretization that only occur in relational learning problems (when indeterminate literals occur). The second technique is lookahead. It is a well-known problem in ILP that a learner cannot always assess the quality of a refinement without knowing which refinements will be enabled afterwards, i.e. without looking ahead in the refinement lattice. We present a simple method for specifying when lookahead is to be used, and what kind of lookahead is interesting. Both the discretization and lookahead techniques are evaluated experimentally. The results show that both techniques improve the quality of the induced theory, while computational costs are acceptable. Please summarise all the given information.

Paper #3218 has the title `Rule Induction with CN2: Some Recent Improvements  
` and the abstract `The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan's C4.5 are also made. 
`.
Question: Which category should Paper #3218 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #3218 has the title `Rule Induction with CN2: Some Recent Improvements  
` and the abstract `The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan's C4.5 are also made. 
`.
Question: Which category should Paper #3218 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Rule_Learning