Above are papers related to the following paper: [Paper_ID] Paper #1123867. 	[Title] A Study of Maximal-Coverage Learning Algorithms [Abstract] The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any Boolean concept learning algorithm and describes two algorithms|Multi-Balls and Large-Ball|whose coverage approaches this upper bound. Experimental measurement of the coverage of the ID3 and FRINGE algorithms shows that their coverage is far below this bound. Further analysis of Large-Ball shows that although it learns many concepts, these do not seem to be very interesting concepts. Hence, coverage maximization alone does not appear to yield practically-useful learning algorithms. The paper concludes with a definition of coverage within a bias, which suggests a way that coverage maximization could be applied to strengthen weak preference biases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6917. 	[Title] Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs [Abstract] covering has been formalized and used extensively. In this work, the divide-and-conquer technique is formalized as well and compared to the covering technique in a logic programming framework. Covering works by repeatedly specializing an overly general hypothesis, on each iteration focusing on finding a clause with a high coverage of positive examples. Divide-and-conquer works by specializing an overly general hypothesis once, focusing on discriminating positive from negative examples. Experimental results are presented demonstrating that there are cases when more accurate hypotheses can be found by divide-and-conquer than by covering. Moreover, since covering considers the same alternatives repeatedly it tends to be less efficient than divide-and-conquer, which never considers the same alternative twice. On the other hand, covering searches a larger hypothesis space, which may result in that more compact hypotheses are found by this technique than by divide-and-conquer. Furthermore, divide-and-conquer is, in contrast to covering, not applicable to learn ing recursive definitions. 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 #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 #114189. 	[Title] Searching for dependencies in Bayesian classifiers j A n V n j If the attributes [Abstract] Naive Bayesian classifiers which make independence assumptions perform remarkably well on some data sets but poorly on others. We explore ways to improve the Bayesian classifier by searching for dependencies among attributes. We propose and evaluate two algorithms for detecting dependencies among attributes and show that the backward sequential elimination and joining algorithm provides the most improvement over the naive Bayesian classifier. The domains on which the most improvement occurs are those domains on which the naive Bayesian classifier is significantly less accurate than a decision tree learner. This suggests that the attributes used in some common databases are not independent conditioned on the class and that the violations of the independence assumption that affect the accuracy of the classifier The Bayesian classifier (Duda & Hart, 1973) is a probabilistic method for classification. It can be used to determine the probability that an example j belongs to class C i given values of attributes of an example represented as a set of n nominally-valued attribute-value pairs of the form A 1 = V 1 j : ^ P (A k = V k j jC i ) may be estimated from the training data. To determine the most likely class of a test example, the probability of each class is computed with Equation 1. A classifier created in this manner is sometimes called a simple (Langley, 1993) or naive (Kononenko, 1990) Bayesian classifier. One important evaluation metric for machine learning methods is the predictive accuracy on unseen examples. This is measured by randomly selecting a subset of the examples in a database to use as training examples and reserving the remainder to be used as test examples. In the case of the simple Bayesian classifier, the training examples are used to estimate probabilities and Equation 1.1 is then used can be detected from training data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135358. 	[Title] 0 Inductive learning of compact rule sets by using efficient hypotheses reduction [Abstract] A method is described which reduces the hypotheses space with an efficient and easily interpretable reduction criteria called a - reduction. A learning algorithm is described based on a - reduction and analyzed by using probability approximate correct learning results. The results are obtained by reducing a rule set to an equivalent set of kDNF formulas. The goal of the learning algorithm is to induce a compact rule set describing the basic dependencies within a set of data. The reduction is based on criterion which is very exible and gives a semantic interpretation of the rules which fulfill the criteria. Comparison with syntactical hypotheses reduction show that the a reduction improves search and has a smaller probability of missclassification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135125. 	[Title] Constructing Fuzzy Graphs from Examples [Abstract] Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an efficient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs offer quick insights into the structure of the example data, that is, the underlying model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #158614. 	[Title] Efficient Algorithms for Identifying Relevant Features [Abstract] This paper describes efficient methods for exact and approximate implementation of the MIN-FEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This bias is useful for learning domains where many irrelevant features are present in the training data. We first introduce FOCUS-2, a new algorithm that exactly implements the MIN-FEATURES bias. This algorithm is empirically shown to be substantially faster than the FOCUS algorithm previously given in [ Al-muallim and Dietterich, 1991 ] . We then introduce the Mutual-Information-Greedy, Simple-Greedy and Weighted-Greedy algorithms, which apply efficient heuristics for approximating the MIN-FEATURES bias. These algorithms employ greedy heuristics that trade optimality for computational efficiency. Experimental studies show that the learning performance of ID3 is greatly improved when these algorithms are used to preprocess the training data by eliminating the irrelevant features from ID3's consideration. In particular, the Weighted-Greedy algorithm provides an excellent and efficient approximation of the MIN Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #552469. 	[Title] On Learning More Concepts [Abstract] The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any Boolean concept learning algorithm and describes two algorithms|Multi-Balls and Large-Ball|whose coverage approaches this upper bound. Experimental measurement of the coverage of the ID3 and FRINGE algorithms shows that their coverage is far below this bound. Further analysis of Large-Ball shows that although it learns many concepts, these do not seem to be very interesting concepts. Hence, coverage maximization alone does not appear to yield practically-useful learning algorithms. The paper concludes with a definition of coverage within a bias, which suggests a way that coverage maximization could be applied to strengthen weak preference biases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #520471. 	[Title] The Arguments of Newly Invented Predicates in ILP [Abstract] In this paper we investigate the problem of choosing arguments for a new predicate. We identify the relevant terms to be considered as arguments, and propose methods to choose among them based on propositional minimisation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3231. 	[Title] Irrelevant Features and the Subset Selection Problem [Abstract] We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102625. 	[Title] A systematic description of greedy optimisation algorithms for cost sensitive generalisation [Abstract] This paper defines a class of problems involving combinations of induction and (cost) optimisation. A framework is presented that systematically describes problems that involve construction of decision trees or rules, optimising accuracy as well as measurement- and misclassification costs. It does not present any new algorithms but shows how this framework can be used to configure greedy algorithms for constructing such trees or rules. The framework covers a number of existing algorithms. Moreover, the framework can also be used to define algorithm configurations with new functionalities, as expressed in their evaluation functions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #211875. 	[Title] Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools [Abstract] We introduce a parallel approach, "DT-Select," for selecting features used by inductive learning algorithms to predict protein secondary structure. DT-Select is able to rapidly choose small, nonredundant feature sets from pools containing hundreds of thousands of potentially useful features. It does this by building a decision tree, using features from the pool, that classifies a set of training examples. The features included in the tree provide a compact description of the training data and are thus suitable for use as inputs to other inductive learning algorithms. Empirical experiments in the protein secondary-structure task, in which sets of complex features chosen by DT-Select are used to augment a standard artificial neural network representation, yield surprisingly little performance gain, even though features are selected from very large feature pools. We discuss some possible reasons for this result. 1 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #560936. 	[Title] Monte Carlo Comparison of Non-hierarchical Unsupervised Classifiers [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #591016. 	[Title] First Order Regression: Applications in Real-World Domains [Abstract] A first order regression algorithm capable of handling real-valued (continuous) variables is introduced and some of its applications are presented. Regressional learning assumes real-valued class and discrete or real-valued variables. The algorithm combines regressional learning with standard ILP concepts, such as first order concept description and background knowledge. A clause is generated by successively refining the initial clause by adding literals of the form A = v for the discrete attributes, A v and A v for the real-valued attributes, and background knowledge literals to the clause body. The algorithm employs a covering approach (beam search), a heuristic impurity function, and stopping criteria based on local improvement, minimum number of examples, maximum clause length, minimum local improvement, minimum description length, allowed error, and variable depth. An outline of the algorithm and the results of the system's application in some artificial and real-world domains are presented. The real-world domains comprise: modelling of the water behavior in a surge tank, modelling of the workpiece roughness in a steel grinding process and modelling of the operator's behavior during the process of electrical discharge machining. Special emphasis is given to the evaluation of obtained models by domain experts and their comments on the aspects of practical use of the induced knowledge. The results obtained during the knowledge acquisition process show several important guidelines for knowledge acquisition, concerning mainly the process of interaction with domain experts, exposing primarily the importance of comprehensibility of the induced knowledge. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #711598. 	[Title] Pattern Theoretic Knowledge Discovery [Abstract] Future research directions in Knowledge Discovery in Databases (KDD) include the ability to extract an overlying concept relating useful data. Current limitations involve the search complexity to find that concept and what it means to be "useful." The Pattern Theory research crosses over in a natural way to the aforementioned domain. The goal of this paper is threefold. First, we present a new approach to the problem of learning by Discovery and robust pattern finding. Second, we explore the current limitations of a Pattern Theoretic approach as applied to the general KDD problem. Third, we exhibit its performance with experimental results on binary functions, and we compare those results with C4.5. This new approach to learning demonstrates a powerful method for finding patterns in a robust manner. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30895. 	[Title] Induction of Oblique Decision Trees [Abstract] This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques, such as ID3 and its descendants, partition a set of points with axis-parallel hyper-planes. Our method, by contrast, attempts to find hyperplanes at any orientation. The purpose of this more general technique is to find smaller but equally accurate decision trees than those created by other methods. We have tested our algorithm on both real and simulated data, and found that in some cases it produces surprisingly small trees without losing predictive accuracy. Small trees allow us, in turn, to obtain simple qualitative descriptions of each problem domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129208. 	[Title] Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and [Abstract] that is based on Angluin's L fl algorithm. The algorithm maintains a model consistent with its past examples. When a new counterexample arrives it tries to extend the model in a minimal fashion. We conducted a set of experiments where random automata that represent different strategies were generated, and the algorithm tried to learn them based on prefix-closed samples of their behavior. The algorithm managed to learn very compact models that agree with the samples. The size of the sample had a small effect on the size of the model. The experimental results suggest that for random prefix-closed samples the algorithm behaves well. However, following Angluin's result on the difficulty of learning almost uniform complete samples [ An-gluin, 1978 ] , it is obvious that our algorithm does not solve the complexity issue of inferring a DFA from a general prefix-closed sample. We are currently looking for classes of prefix-closed samples in which US-L* behaves well. [ Carmel and Markovitch, 1994 ] D. Carmel and S. Markovitch. The M* algorithm: Incorporating opponent models into adversary search. Technical Report CIS report 9402, Technion, March 1994. [ Carmel and Markovitch, 1995 ] D. Carmel and S. Markovitch. Unsupervised learning of finite automata: A practical approach. Technical Report CIS report 9504, Technion, March 1995. [ Shoham and Tennenholtz, 1994 ] Y. Shoham and M. Tennenholtz. Co-Learning and the evolution of social activity. Technical Report STAN-CS-TR-94-1511, Stanford Univrsity, Department of Computer Science, 1994. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30901. 	[Title] Lookahead and Pathology in Decision Tree Induction [Abstract] The standard approach to decision tree induction is a top-down, greedy algorithm that makes locally optimal, irrevocable decisions at each node of a tree. In this paper, we empirically study an alternative approach, in which the algorithms use one-level lookahead to decide what test to use at a node. We systematically compare, using a very large number of real and artificial data sets, the quality of decision trees induced by the greedy approach to that of trees induced using lookahead. The main observations from our experiments are: (i) the greedy approach consistently produced trees that were just as accurate as trees produced with the much more expensive lookahead step; and (ii) we observed many instances of pathology, i.e., lookahead produced trees that were both larger and less accurate than trees produced without it. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #259126. 	[Title] A Monotonic Measure for Optimal Feature Selection [Abstract] Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distance-based measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed to be complete but not exhaustive. Experiments are conducted for verification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18615. 	[Title] Learning Trees and Rules with Set-valued Features [Abstract] In most learning systems examples are represented as fixed-length "feature vectors", the components of which are either real numbers or nominal values. We propose an extension of the feature-vector representation that allows the value of a feature to be a set of strings; for instance, to represent a small white and black dog with the nominal features size and species and the set-valued feature color, one might use a feature vector with size=small, species=canis-familiaris and color=fwhite,blackg. Since we make no assumptions about the number of possible set elements, this extension of the traditional feature-vector representation is closely connected to Blum's "infinite attribute" representation. We argue that many decision tree and rule learning algorithms can be easily extended to set-valued features. We also show by example that many real-world learning problems can be efficiently and naturally represented with set-valued features; in particular, text categorization problems and problems that arise in propositionalizing first-order representations lend themselves to set-valued features. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85688. 	[Title] Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings [Abstract] addressed in KBANN (which translates a theory into a neural-net, refines it using backpropagation, and then retranslates the result back into rules) by adding extra hidden units and connections to the initial network; however, this would require predetermining the num In this paper, we have presented constructive induction techniques recently added to the EITHER theory refinement system. Intermediate concept utilization employs existing rules in the theory to derive higher-level features for use in induction. Intermediate concept creation employs inverse resolution to introduce new intermediate concepts in order to fill gaps in a theory than span multiple levels. These revisions allow EITHER to make use of imperfect domain theories in the ways typical of previous work in both constructive induction and theory refinement. As a result, EITHER is able to handle a wider range of theory imperfections than any other existing theory refinement system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10169. 	[Title] Learning Boolean Concepts in the Presence of Many Irrelevant Features [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63486. 	[Title] Evaluation and Selection of Biases in Machine Learning [Abstract] In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias selection as search in bias and meta-bias spaces. Recent research in the field of machine learning bias is summarized. 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 #1128256. 	[Title] Where Do SE-trees Perform? (Part I) [Abstract] As a classifier, a Set Enumeration (SE) tree can be viewed as a generalization of decision trees. We empirically characterize domains in which SE-trees are particularly advantageous relative to decision trees. Specifically, we show that: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10177. 	[Title] Learning in the Presence of Malicious Errors [Abstract] In this paper we study an extension of the distribution-free model of learning introduced by Valiant [23] (also known as the probably approximately correct or PAC model) that allows the presence of malicious errors in the examples given to a learning algorithm. Such errors are generated by an adversary with unbounded computational power and access to the entire history of the learning algorithm's computation. Thus, we study a worst-case model of errors. Our results include general methods for bounding the rate of error tolerable by any learning algorithm, efficient algorithms tolerating nontrivial rates of malicious errors, and equivalences between problems of learning with errors and standard combinatorial optimization problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120962. 	[Title] Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and [Abstract] This chapter describes three studies which address the question of how neural network learning can be improved via the incorporation of information extracted from other networks. This general problem, which we call network transfer, encompasses many types of relationships between source and target networks. Our focus is on the utilization of weights from source networks which solve a subproblem of the target network task, with the goal of speeding up learning on the target task. We demonstrate how the approach described here can improve learning speed by up to ten times over learning starting with random weights. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #636098. 	[Title] NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all [Abstract] In many learning problems, the learning system is presented with values for features that are actually irrelevant to the concept it is trying to learn. The FOCUS algorithm, due to Almuallim and Dietterich, performs an explicit search for the smallest possible input feature set S that permits a consistent mapping from the features in S to the output feature. The FOCUS algorithm can also be seen as an algorithm for learning determinations or functional dependencies, as suggested in [6]. Another algorithm for learning determinations appears in [7]. The FOCUS algorithm has superpolynomial runtime, but Almuallim and Di-etterich leave open the question of tractability of the underlying problem. In this paper, the problem is shown to be NP-complete. We also describe briefly some experiments that demonstrate the benefits of determination learning, and show that finding lowest-cardinality determinations is easier in practice than finding minimal determi Define the MIN-FEATURES problem as follows: given a set X of examples (which are each composed of a a binary value specifying the value of the target feature and a vector of binary values specifying the values of the other features) and a number n, determine whether or not there exists some feature set S such that: We show that MIN-FEATURES is NP-complete by reducing VERTEX-COVER to MIN-FEATURES. 1 The VERTEX-COVER problem may be stated as the question: given a graph G with vertices V and edges E, is there a subset V 0 of V , of size m, such that each edge in E is connected to at least one vertex in V 0 ? We may reduce an instance of VERTEX-COVER to an instance of MIN-FEATURES by mapping each edge in E to an example in X, with one input feature for every vertex in V . 1 In [8], a "proof" is reported for this result by reduction to set covering. The proof therefore fails to show NP-completeness. nations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15429. 	[Title] A General Lower Bound on the Number of Examples Needed for Learning [Abstract] We prove a lower bound of ( 1 * ln 1 ffi + VCdim(C) * ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and * and ffi are the accuracy and confidence parameters. This improves the previous best lower bound of ( 1 * ln 1 ffi + VCdim(C)), and comes close to the known general upper bound of O( 1 ffi + VCdim(C) * ln 1 * ) for consistent algorithms. We show that for many interesting concept classes, including kCNF and kDNF, our bound is actually tight to within a constant factor. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108167. 	[Title] On Learning Read-k-Satisfy-j DNF [Abstract] We study the learnability of Read-k-Satisfy-j (RkSj) DNF formulas. These are boolean formulas in disjunctive normal form (DNF), in which the maximum number of occurrences of a variable is bounded by k, and the number of terms satisfied by any assignment is at most j. After motivating the investigation of this class of DNF formulas, we present an algorithm that with high probability finds a DNF formula that is logically equivalent to any unknown RkSj DNF formula to be learned. The algorithm uses the well-studied protocol of equivalence and membership queries, and runs in polynomial time for k j = O( log n log log n ), where n is the number of input variables. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #254923. 	[Title] A genetic prototype learner [Abstract] Supervised classification problems have received considerable attention from the machine learning community. We propose a novel genetic algorithm based prototype learning system, PLEASE, for this class of problems. Given a set of prototypes for each of the possible classes, the class of an input instance is determined by the prototype nearest to this instance. We assume ordinal attributes and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space as determined by corresponding feature-value pairs. Comparisons with C4.5 on a set of artificial problems of controlled complexity demonstrate the effectiveness of the pro posed system. 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 #444240. 	[Title] PLEASE: A prototype learning system using genetic algorithms [Abstract] Prototypes have been proposed as representation of concepts that are used effectively by humans. Developing computational schemes for generating prototypes from examples, however, has proved to be a difficult problem. We present a novel genetic algorithm based prototype learning system, PLEASE, for constructing appropriate prototypes from classified training instances. After constructing a set of prototypes for each of the possible classes, the class of a new input instance is determined by the nearest prototype to this instance. Attributes are assumed to be ordinal in nature and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space. We present experimental results on a series of artificial problems of varying complexity. PLEASE performs competitively with several nearest neighbor classification algorithms on the problem set. An analysis of the strengths and weaknesses of the initial version of our system motivates the need for additional operators. The inclusion of these operators substantially improves the performance of the system on particularly difficult problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39126. 	[Title] Toward Optimal Feature Selection [Abstract] In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computation-ally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an efficient algorithm for feature selection which computes an approximation to the optimal feature selection criterion. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm effectively han dles datasets with large numbers of features. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39131. 	[Title] Oblivious Decision Trees and Abstract Cases [Abstract] In this paper, we address the problem of case-based learning in the presence of irrelevant features. We review previous work on attribute selection and present a new algorithm, Oblivion, that carries out greedy pruning of oblivious decision trees, which effectively store a set of abstract cases in memory. We hypothesize that this approach will efficiently identify relevant features even when they interact, as in parity concepts. We report experimental results on artificial domains that support this hypothesis, and experiments with natural domains that show improvement in some cases but not others. In closing, we discuss the implications of our experiments, consider additional work on irrelevant features, and outline some directions for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #192734. 	[Title] Preventing "Overfitting" of Cross-Validation Data [Abstract] Suppose that, for a learning task, we have to select one hypothesis out of a set of hypotheses (that may, for example, have been generated by multiple applications of a randomized learning algorithm). A common approach is to evaluate each hypothesis in the set on some previously unseen cross-validation data, and then to select the hypothesis that had the lowest cross-validation error. But when the cross-validation data is partially corrupted such as by noise, and if the set of hypotheses we are selecting from is large, then "folklore" also warns about "overfitting" the cross-validation data [Klockars and Sax, 1986, Tukey, 1949, Tukey, 1953]. In this paper, we explain how this "overfitting" really occurs, and show the surprising result that it can be overcome by selecting a hypothesis with a higher cross-validation error, over others with lower cross-validation errors. We give reasons for not selecting the hypothesis with the lowest cross-validation error, and propose a new algorithm, LOOCVCV, that uses a computa-tionally efficient form of leave-one-out cross-validation to select such a hypothesis. Finally, we present experimental results for one domain, that show LOOCVCV consistently beating picking the hypothesis with the lowest cross-validation error, even when using reasonably large cross-validation sets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27230. 	[Title] K unstliche Intelligenz Grdt: Enhancing Model-Based Learning for Its Application in Robot Navigation [Abstract]  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 #97892. 	[Title] Correcting Imperfect Domain Theories: A Knowledge-Level Analysis [Abstract] Explanation-Based Learning [Mitchell et al., 1986; DeJong and Mooney, 1986] has shown promise as a powerful analytical learning technique. However, EBL is severely hampered by the requirement of a complete and correct domain theory for successful learning to occur. Clearly, in non-trivial domains, developing such a domain theory is a nearly impossible task. Therefore, much research has been devoted to understanding how an imperfect domain theory can be corrected and extended during system performance. In this paper, we present a characterization of this problem, and use it to analyze past research in the area. Past characterizations of the problem (e.g, [Mitchell et al., 1986; Rajamoney and DeJong, 1987]) have viewed the types of performance errors caused by a faulty domain theory as primary. In contrast, we focus primarily on the types of knowledge deficiencies present in the theory, and from these derive the types of performance errors that can result. Correcting the theory can be viewed as a search through the space of possible domain theories, with a variety of knowledge sources that can be used to guide the search. We examine the types of knowledge used by a variety of past systems for this purpose. The hope is that this analysis will indicate the need for a "universal weak method" of domain theory correction, in which different sources of knowledge for theory correction can be freely and flexibly combined. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #52835. 	[Title] On Biases in Estimating Multi-Valued Attributes [Abstract] We analyse the biases of eleven measures for estimating the quality of the multi-valued attributes. The values of information gain, J-measure, gini-index, and relevance tend to linearly increase with the number of values of an attribute. The values of gain-ratio, distance measure, Relief , and the weight of evidence decrease for informative attributes and increase for irrelevant attributes. The bias of the statistic tests based on the chi-square distribution is similar but these functions are not able to discriminate among the attributes of different quality. We also introduce a new function based on the MDL principle whose value slightly decreases with the increasing number of attribute's values. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4584. 	[Title] References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages [Abstract] Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #157805. 	[Title] Learning from Queries and Examples with Tree-structured Bias [Abstract] Incorporating declarative bias or prior knowledge into learning is an active research topic in machine learning. Tree-structured bias specifies the prior knowledge as a tree of "relevance" relationships between attributes. This paper presents a learning algorithm that implements tree-structured bias, i.e., learns any target function probably approximately correctly from random examples and membership queries if it obeys a given tree-structured bias. The theoretical predictions of the paper are em pirically validated. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20593. 	[Title] Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract) [Abstract] We investigate the generation of neural networks through the induction of binary trees of threshold logic units (TLUs). Initially, we describe the framework for our tree construction algorithm and how such trees can be transformed into an isomorphic neural network topology. Several methods for learning the linear discriminant functions at each node of the tree structure are examined and shown to produce accuracy results that are comparable to classical information theoretic methods for constructing decision trees (which use single feature tests at each node). Our TLU trees, however, are smaller and thus easier to understand. Moreover, we show that it is possible to simultaneously learn both the topology and weight settings of a neural network simply using the training data set that we are given. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20850. 	[Title] Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise [Abstract] Irrelevant and redundant features may reduce both predictive accuracy and comprehensibility of induced concepts. Most common Machine Learning approaches for selecting a good subset of relevant features rely on cross-validation. As an alternative, we present the application of a particular Minimum Description Length (MDL) measure to the task of feature subset selection. Using the MDL principle allows taking into account all of the available data at once. The new measure is information-theoretically plausible and yet still simple and therefore efficiently computable. We show empirically that this new method for judging the value of feature subsets is more efficient than and performs at least as well as methods based on cross-validation. Domains with both a large number of training examples and a large number of possible features yield the biggest gains in efficiency. Thus our new approach seems to scale up better to large learning problems than previous methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63477. 	[Title] Efficient Feature Selection in Conceptual Clustering [Abstract] Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are discussed and an implementation of a sequential feature selection algorithm based on an existing conceptual clustering system is described. Additionally, we present a second implementation which employs a technique for improving the efficiency of the search for an optimal description and compare the performance of both algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #65653. 	[Title] Pessimistic decision tree pruning based on tree size [Abstract] In this work we develop a new criteria to perform pessimistic decision tree pruning. Our method is theoretically sound and is based on theoretical concepts such as uniform convergence and the Vapnik-Chervonenkis dimension. We show that our criteria is very well motivated, from the theory side, and performs very well in practice. The accuracy of the new criteria is comparable to that of the current method used in C4.5. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13686. 	[Title] Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm [Abstract] Technical Report 1123, Computer Sciences Department, University of Wisconsin - Madison, Nov. 1992 ABSTRACT This article describes an approach to combining symbolic and connectionist approaches to machine learning. A three-stage framework is presented and the research of several groups is reviewed with respect to this framework. The first stage involves the insertion of symbolic knowledge into neural networks, the second addresses the refinement of this prior knowledge in its neural representation, while the third concerns the extraction of the refined symbolic knowledge. Experimental results and open research issues are discussed. A shorter version of this paper will appear in Machine Learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1272. 	[Title] Learning a set of primitive actions with an Induction of decision trees. Machine Learning, 1(1):81-106, [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #112378. 	[Title] Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion [Abstract] We describe an approach to grapheme-to-phoneme conversion which is both language-independent and data-oriented. Given a set of examples (spelling words with their associated phonetic representation) in a language, a grapheme-to-phoneme conversion system is automatically produced for that language which takes as its input the spelling of words, and produces as its output the phonetic transcription according to the rules implicit in the training data. We describe the design of the system, and compare its performance to knowledge-based and alternative data-oriented approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107067. 	[Title] On the Greediness of Feature Selection Algorithms [Abstract] Based on our analysis and experiments using real-world datasets, we find that the greediness of forward feature selection algorithms does not severely corrupt the accuracy of function approximation using the selected input features, but improves the efficiency significantly. Hence, we propose three greedier algorithms in order to further enhance the efficiency of the feature selection processing. We provide empirical results for linear regression, locally weighted regression and k-nearest-neighbor models. We also propose to use these algorithms to develop an offline Chinese and Japanese handwriting recognition system with auto matically configured, local models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #49660. 	[Title] Feature Subset Selection as Search with Probabilistic Estimates [Abstract] Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading off accuracy of estimates for increased state exploration. We show how recent feature subset selection algorithms in the machine learning literature fit into this search problem as simple hill climbing approaches, and conduct a small experiment using a best-first search technique. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #284414. 	[Title] BRACE: A Paradigm For the Discretization of Continuously Valued Data [Abstract] Discretization of continuously valued data is a useful and necessary tool because many learning paradigms assume nominal data. A list of objectives for efficient and effective discretization is presented. A paradigm called BRACE (Boundary Ranking And Classification Evaluation) that attempts to meet the objectives is presented along with an algorithm that follows the paradigm. The paradigm meets many of the objectives, with potential for extension to meet the remainder. Empirical results have been promising. For these reasons BRACE has potential as an effective and efficient method for discretization of continuously valued data. A further advantage of BRACE is that it is general enough to be extended to other types of clustering/unsupervised learning. Please summarise all the given information.

Paper #1123867 has the title `A Study of Maximal-Coverage Learning Algorithms  
` and the abstract `The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any Boolean concept learning algorithm and describes two algorithms|Multi-Balls and Large-Ball|whose coverage approaches this upper bound. Experimental measurement of the coverage of the ID3 and FRINGE algorithms shows that their coverage is far below this bound. Further analysis of Large-Ball shows that although it learns many concepts, these do not seem to be very interesting concepts. Hence, coverage maximization alone does not appear to yield practically-useful learning algorithms. The paper concludes with a definition of coverage within a bias, which suggests a way that coverage maximization could be applied to strengthen weak preference biases.
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Question: Which category should Paper #1123867 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1123867 has the title `A Study of Maximal-Coverage Learning Algorithms  
` and the abstract `The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any Boolean concept learning algorithm and describes two algorithms|Multi-Balls and Large-Ball|whose coverage approaches this upper bound. Experimental measurement of the coverage of the ID3 and FRINGE algorithms shows that their coverage is far below this bound. Further analysis of Large-Ball shows that although it learns many concepts, these do not seem to be very interesting concepts. Hence, coverage maximization alone does not appear to yield practically-useful learning algorithms. The paper concludes with a definition of coverage within a bias, which suggests a way that coverage maximization could be applied to strengthen weak preference biases.
`.
Question: Which category should Paper #1123867 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: Theory