Above are papers related to the following paper: [Paper_ID] Paper #20942. 	[Title] Decision Tree Induction Based on Efficient Tree Restructuring [Abstract] The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #93320. 	[Title] Some Experiments with a Hybrid Model for Learning Sequential Decision Making [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #180373. 	[Title] CANCER DIAGNOSIS AND PROGNOSIS VIA LINEAR-PROGRAMMING-BASED MACHINE LEARNING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106330. 	[Title] Induction of Condensed Determinations [Abstract] In this paper we suggest determinations as a representation of knowledge that should be easy to understand. We briefly review determinations, which can be displayed in a tabular format, and their use in prediction, which involves a simple matching process. We describe ConDet, an algorithm that uses feature selection to construct determinations from training data, augmented by a condensation process that collapses rows to produce simpler structures. We report experiments that show condensation reduces complexity with no loss of accuracy, then discuss ConDet's relation to other work and outline directions for future studies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3232. 	[Title] The Power of Decision Tables [Abstract] We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5. Surprisingly, performance is quite good on some datasets with continuous features, indicating that many datasets used in machine learning either do not require these features, or that these features have few values. We also describe an incremental method for performing cross-validation that is applicable to incremental learning algorithms including IDTM. Using incremental cross-validation, it is possible to cross-validate a given dataset and IDTM in time that is linear in the number of instances, the number of features, and the number of label values. The time for incremental cross-validation is independent of the number of folds chosen, hence leave-one-out cross-validation and ten-fold cross-validation take the same time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #66594. 	[Title] Automatic Parameter Selection by Minimizing Estimated Error [Abstract] We address the problem of finding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a "wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses best-first search and cross-validation to wrap around the basic induction algorithm: the search explores the space of parameter values, running the basic algorithm many times on training and holdout sets produced by cross-validation to get an estimate of the expected error of each parameter setting. Thus, the final selected parameter settings are tuned for the specific induction algorithm and dataset being studied. We report experiments with this method on 33 datasets selected from the UCI and StatLog collections using C4.5 as the basic induction algorithm. At a 90% confidence level, our method improves the performance of C4.5 on nine domains, degrades performance on one, and is statistically indistinguishable from C4.5 on the rest. On the sample of datasets used for comparison, our method yields an average 13% relative decrease in error rate. We expect to see similar performance improvements when using our method with other machine learning al gorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521251. 	[Title] A Yardstick for the Evaluation of Case-Based Classifiers [Abstract] This paper proposes that the generalisation capabilities of a case-based reasoning system can be evaluated by comparison with a `rote-learning' algorithm which uses a very simple generalisation strategy. Two such algorithms are defined, and expressions for their classification accuracy are derived as a function of the size of training sample. A series of experiments using artificial and `natural' data sets is described in which the learning curve for a case-based learner is compared with those for the apparently trivial rote-learning learning algorithms. The results show that in a number of `plausible' situations, the learning curves for a simple case-based learner and the `majority' rote-learner can barely be distinguished, although a domain is demonstrated where favourable performance from the case-based learner is observed. This suggests that the maxim of case-based reasoning that `similar problems have similar solutions' may be useful as the basis of a generalisation strategy only in selected domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110947. 	[Title] Lazy Induction Triggered by CBR [Abstract] In recent years, case-based reasoning has been demonstrated to be highly useful for problem solving in complex domains. Also, mixed paradigm approaches emerged for combining CBR and induction techniques aiming at verifying the knowledge and/or building an efficient case memory. However, in complex domains induction over the whole problem space is often not possible or too time consuming. In this paper, an approach is presented which (owing to a close interaction with the CBR part) attempts to induce rules only for a particular context, i.e. for a problem just being solved by a CBR-oriented system. These rules may then be used for indexing purposes or similarity assessment in order to support the CBR process in the future. 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 #10798. 	[Title] Learning physical descriptions from functional definitions, examples, Learning from examples: The effect of different conceptual [Abstract]  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 #1153148. 	[Title] Learning Classification Trees [Abstract] Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to Quinlan's information gain, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 (Quinlan et al., 1987) and Breiman et al.'s CART (Breiman et al., 1984) show the full Bayesian algorithm can produce Publication: This paper is a final draft submitted for publication to the Statistics and Computing journal; a version with some minor changes appeared in Volume 2, 1992, pages 63-73. more accurate predictions than versions of these other approaches, though pay a computational price. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6213. 	[Title] Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction, [Abstract] This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage. 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 #46536. 	[Title] Linear Machine Decision Trees [Abstract] COINS Technical Report 91-10 January 1991 Abstract This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6217. 	[Title] Online Learning with Random Representations [Abstract] We consider the requirements of online learning|learning which must be done incrementally and in realtime, with the results of learning available soon after each new example is acquired. Despite the abundance of methods for learning from examples, there are few that can be used effectively for online learning, e.g., as components of reinforcement learning systems. Most of these few, including radial basis functions, CMACs, Ko-honen's self-organizing maps, and those developed in this paper, share the same structure. All expand the original input representation into a higher dimensional representation in an unsupervised way, and then map that representation to the final answer using a relatively simple supervised learner, such as a perceptron or LMS rule. Such structures learn very rapidly and reliably, but have been thought either to scale poorly or to require extensive domain knowledge. To the contrary, some researchers (Rosenblatt, 1962; Gallant & Smith, 1987; Kanerva, 1988; Prager & Fallside, 1988) have argued that the expanded representation can be chosen largely at random with good results. The main contribution of this paper is to develop and test this hypothesis. We show that simple random-representation methods can perform as well as nearest-neighbor methods (while being more suited to online learning), and significantly better than backpropagation. We find that the size of the random representation does increase with the dimensionality of the problem, but not unreasonably so, and that the required size can be reduced substantially using unsupervised-learning techniques. Our results suggest that randomness has a useful role to play in online supervised learning and constructive induction. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113934. 	[Title] Integration of Case-Based Reasoning and Neural Networks Approaches for Classification [Abstract] Several different approaches have been used to describe concepts for supervised learning tasks. In this paper we describe two approaches which are: prototype-based incremental neural networks and case-based reasoning approaches. We show then how we can improve a prototype-based neural network model by storing some specific instances in a CBR memory system. This leads us to propose a co-processing hybrid model for classification. 1 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128536. 	[Title] MLC Tutorial A Machine Learning library of C classes. [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #601567. 	[Title] Geometric Comparison of Classifications and Rule Sets* [Abstract] We present a technique for evaluating classifications by geometric comparison of rule sets. Rules are represented as objects in an n-dimensional hyperspace. The similarity of classes is computed from the overlap of the geometric class descriptions. The system produces a correlation matrix that indicates the degree of similarity between each pair of classes. The technique can be applied to classifications generated by different algorithms, with different numbers of classes and different attribute sets. Experimental results from a case study in a medical domain are included. 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 #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31097. 	[Title] What Daimler-Benz has learned as an industrial partner from the Machine Learning Project StatLog [Abstract] Author of this paper was co-ordinator of the Machine Learning project StatLog during 1990-1993. This project was supported financially by the European Community. The main aim of StatLog was to evaluate different learning algorithms using real industrial and commercial applications. As an industrial partner and contributor, Daimler-Benz has introduced different applications to Stat-Log among them fault diagnosis, letter and digit recognition, credit-scoring and prediction of the number of registered trucks. We have learned a lot of lessons from this project which have effected our application oriented research in the field of Machine Learning (ML) in Daimler-Benz. We have distinguished that, especially, more research is necessary to prepare the ML-algorithms to handle the real industrial and commercial applications. In this paper we describe, shortly, the Daimler-Benz applications in StatLog, we discuss shortcomings of the applied ML-algorithms and finally we outline the fields where we think further research is necessary. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8699. 	[Title] Theory Revision in Fault Hierarchies [Abstract] The fault hierarchy representation is widely used in expert systems for the diagnosis of complex mechanical devices. On the assumption that an appropriate bias for a knowledge representation language is also an appropriate bias for learning in this domain, we have developed a theory revision method that operates directly on a fault hierarchy. This task presents several challenges: A typical training instance is missing most feature values, and the pattern of missing features is significant, rather than merely an effect of noise. Moreover, the accuracy of a candidate theory is measured by considering both the sequence of tests required to arrive at a diagnosis and its agreement with the diagnostic endpoints provided by an expert. This paper first describes the algorithm for theory revision of fault hierarchies that was designed to address these challenges, then discusses its application in knowledge base maintenance and reports on experiments that use to revise a fielded diagnostic system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123068. 	[Title] Targeting Business Users with Decision Table Classifiers [Abstract] Business users and analysts commonly use spreadsheets and 2D plots to analyze and understand their data. On-line Analytical Processing (OLAP) provides these users with added flexibility in pivoting data around different attributes and drilling up and down the multi-dimensional cube of aggregations. Machine learning researchers, however, have concentrated on hypothesis spaces that are foreign to most users: hyper-planes (Perceptrons), neural networks, Bayesian networks, decision trees, nearest neighbors, etc. In this paper we advocate the use of decision table classifiers that are easy for line-of-business users to understand. We describe several variants of algorithms for learning decision tables, compare their performance, and describe a visualization mechanism that we have implemented in MineSet. The performance of decision tables is comparable to other known algorithms, such as C4.5/C5.0, yet the resulting classifiers use fewer attributes and are more comprehensible. 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.

Paper #20942 has the title `Decision Tree Induction Based on Efficient Tree Restructuring  
` and the abstract `The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications. 
`.
Question: Which category should Paper #20942 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #20942 has the title `Decision Tree Induction Based on Efficient Tree Restructuring  
` and the abstract `The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications. 
`.
Question: Which category should Paper #20942 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