Above are papers related to the following paper: [Paper_ID] Paper #1125992. 	[Title] Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the [Abstract] The effectiveness of a case-based reasoning system is known to depend critically on its similarity measure. However, it is not clear whether there are elusive and esoteric similarity measures which might improve the performance of a case-based reasoner if substituted for the more commonly used measures. This paper therefore deals with the problem of choosing the best similarity measure, in the limited context of instance-based learning of classifications of a discrete example space. We consider both `fixed' similarity measures and `learnt' ones. In the former case, we give a definition of a similarity measure which we believe to be `optimal' w.r.t. the current prior distribution of target concepts and prove its optimality within a restricted class of similarity measures. We then show how this `optimal' similarity measure is instantiated by some specific prior distributions, and conclude that a very simple similarity measure is as good as any other in these cases. In a further section, we then show how our definition leads naturally to a conjecture about the Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8832. 	[Title] The Possible Contribution of AI to the Avoidance of Crises and Wars: Using CBR Methods [Abstract] This paper presents the application of Case-Based Reasoning methods to the KOSIMO data base of international conflicts. A Case-Based Reasoning tool - VIE-CBR has been deveolped and used for the classification of various outcome variables, like political, military, and territorial outcome, solution modalities, and conflict intensity. In addition, the case retrieval algorithms are presented as an interactive, user-modifiable tool for intelli gently searching the conflict data base for precedent cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #346243. 	[Title] Inductive Bias in Case-Based Reasoning Systems [Abstract] In order to learn more about the behaviour of case-based reasoners as learning systems, we form-alise a simple case-based learner as a PAC learning algorithm, using the case-based representation hCB; i. We first consider a `naive' case-based learning algorithm CB1( H ) which learns by collecting all available cases into the case-base and which calculates similarity by counting the number of features on which two problem descriptions agree. We present results concerning the consistency of this learning algorithm and give some partial results regarding its sample complexity. We are able to characterise CB1( H ) as a `weak but general' learning algorithm. We then consider how the sample complexity of case-based learning can be reduced for specific classes of target concept by the application of inductive bias, or prior knowledge of the class of target concepts. Following recent work demonstrating how case-based learning can be improved by choosing a similarity measure appropriate to the concept being learnt, we define a second case-based learning `algorithm' CB2 which learns using the best possible similarity measure that might be inferred for the chosen target concept. While CB2 is not an executable learning strategy (since the chosen similarity measure is defined in terms of a priori knowledge of the actual target concept) it allows us to assess in the limit the maximum possible contribution of this approach to case-based learning. Also, in addition to illustrating the role of inductive bias, the definition of CB2 simplifies the general problem of establishing which functions might be represented in the form hCB; i. Reasoning about the case-based representation in this special case has therefore been a little more straight-forward than in the general case of CB1( H ), allowing more substantial results regarding representable functions and sample complexity to be presented for CB2. In assessing these results, we are forced to conclude that case-based learning is not the best approach to learning the chosen concept space (the space of monomial functions). We discuss, however, how our study has demonstrated, in the context of case-based learning, the operation of concepts well known in machine learning such as inductive bias and the trade-off between computational complexity and sample complexity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130634. 	[Title] Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract) [Abstract] In this paper we study the performance of probabilistic networks in the context of protein sequence analysis in molecular biology. Specifically, we report the results of our initial experiments applying this framework to the problem of protein secondary structure prediction. One of the main advantages of the probabilistic approach we describe here is our ability to perform detailed experiments where we can experiment with different models. We can easily perform local substitutions (mutations) and measure (probabilistically) their effect on the global structure. Window-based methods do not support such experimentation as readily. Our method is efficient both during training and during prediction, which is important in order to be able to perform many experiments with different networks. We believe that probabilistic methods are comparable to other methods in prediction quality. In addition, the predictions generated by our methods have precise quantitative semantics which is not shared by other classification methods. Specifically, all the causal and statistical independence assumptions are made explicit in our networks thereby allowing biologists to study and experiment with different causal models in a convenient manner. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36620. 	[Title] Towards a Better Understanding of Memory-Based Reasoning Systems [Abstract] We quantify both experimentally and analytically the performance of memory-based reasoning (MBR) algorithms. To start gaining insight into the capabilities of MBR algorithms, we compare an MBR algorithm using a value difference metric to a popular Bayesian classifier. These two approaches are similar in that they both make certain independence assumptions about the data. However, whereas MBR uses specific cases to perform classification, Bayesian methods summarize the data probabilistically. We demonstrate that a particular MBR system called Pebls works comparatively well on a wide range of domains using both real and artificial data. With respect to the artificial data, we consider distributions where the concept classes are separated by functional discriminants, as well as time-series data generated by Markov models of varying complexity. Finally, we show formally that Pebls can learn (in the limit) natural concept classes that the Bayesian classifier cannot learn, and that it will attain perfect accuracy whenever Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3220. 	[Title] A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features [Abstract] In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior in all three domains. In addition, our algorithm has advantages in training speed, simplicity, and perspicuity. We conclude that experimental evidence favors the use and continued development of nearest neighbor algorithms for domains such as the ones studied here. 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 #359067. 	[Title] Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning [Abstract] Conversational case-based reasoning (CCBR) has been successfully used to assist in case retrieval tasks. However, behavioral limitations of CCBR motivate the search for integrations with other reasoning approaches. This paper briefly describes our group's ongoing efforts towards enhancing the inferencing behaviors of a conversational case-based reasoning development tool named NaCoDAE. In particular, we focus on integrating NaCoDAE with machine learning, model-based reasoning, and generative planning modules. This paper defines CCBR, briefly summarizes the integrations, and explains how they enhance the overall system. Our research focuses on enhancing the performance of conversational case-based reasoning (CCBR) systems (Aha & Breslow, 1997). CCBR is a form of case-based reasoning where users initiate problem solving conversations by entering an initial problem description in natural language text. This text is assumed to be a partial rather than a complete problem description. The CCBR system then assists in eliciting refinements of this description and in suggesting solutions. Its primary purpose is to provide a focus of attention for the user so as to quickly provide a solution(s) for their problem. Figure 1 summarizes the CCBR problem solving cycle. Cases in a CCBR library have three components: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115677. 	[Title] Comparison of Regression Methods, Symbolic Induction Methods and Neural Networks in Morbidity Diagnosis and Mortality [Abstract] Classifier induction algorithms differ on what inductive hypotheses they can represent, and on how they search their space of hypotheses. No classifier is better than another for all problems: they have selective superiority. This paper empirically compares six classifier induction algorithms on the diagnosis of equine colic and the prediction of its mortality. The classification is based on simultaneously analyzing sixteen features measured from a patient. The relative merits of the algorithms (linear regression, decision trees, nearest neighbor classifiers, the Model Class Selection system, logistic regression (with and without feature selection), and neural nets) are qualitatively discussed, and the generalization accuracies quantitatively analyzed. 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 #200480. 	[Title] CBET: a Case Base Exploration Tool [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132706. 	[Title] Exemplar-based Music Structure Recognition [Abstract] We tend to think of what we really know as what we can talk about, and disparage knowledge that we can't verbalize. [Dowling 1989, p. 252] 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 #521252. 	[Title] Similarity Metrics: A Formal Unification of Cardinal and Non-Cardinal Similarity Measures [Abstract] In [9] we introduced a formal framework for constructing ordinal similarity measures, and suggested how this might also be applied to cardinal measures. In this paper we will place this approach in a more general framework, called similarity metrics. In this framework, ordinal similarity metrics (where comparison returns a boolean value) can be combined with cardinal metrics (returning a numeric value) and, indeed, with metrics returning values of other types, to produce new metrics. 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 #141868. 	[Title] ABSTRACTION CONSIDERED HARMFUL: LAZY LEARNING OF LANGUAGE PROCESSING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #145215. 	[Title] Memory-Based Lexical Acquisition and Processing [Abstract] Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability bottlenecks. As an alternative, we propose a particular performance-oriented approach to Natural Language Processing based on automatic memory-based learning of linguistic (lexical) tasks. The consequences of the approach for computational lexicology are discussed, and the application of the approach on a number of lexical acquisition and disambiguation tasks in phonology, morphology and syntax is described. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6343. 	[Title] A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms [Abstract] Many lazy learning algorithms are derivatives of the k-nearest neighbor (k-NN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown that k-NN's performance is highly sensitive to the definition of its distance function. Many k-NN variants have been proposed to reduce this sensitivity by parameterizing the distance function with feature weights. However, these variants have not been categorized nor empirically compared. This paper reviews a class of weight-setting methods for lazy learning algorithms. We introduce a framework for distinguishing these methods and empirically compare them. We observed four trends from our experiments and conducted further studies to highlight them. Our results suggest that methods which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings. We also found that continuous weighting methods tend to outperform feature selection algorithms for tasks where some features are useful but less important than others. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131728. 	[Title] Fast NP Chunking Using Memory-Based Learning Techniques [Abstract] In this paper we discuss the application of Memory-Based Learning (MBL) to fast NP chunking. We first discuss the application of a fast decision tree variant of MBL (IGTree) on the dataset described in (Ramshaw and Marcus, 1995), which consists of roughly 50,000 test and 200,000 train items. In a second series of experiments we used an architecture of two cascaded IGTrees. In the second level of this cascaded classifier we added context predictions as extra features so that incorrect predictions from the first level can be corrected, yielding a 97.2% generalisation accuracy with training and testing times in the order of seconds to minutes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39124. 	[Title] The Utility of Feature Weighting in Nearest-Neighbor Algorithms [Abstract] Nearest-neighbor algorithms are known to depend heavily on their distance metric. In this paper, we investigate the use of a weighted Euclidean metric in which the weight for each feature comes from a small set of options. We describe Diet, an algorithm that directs search through a space of discrete weights using cross-validation error as its evaluation function. Although a large set of possible weights can reduce the learner's bias, it can also lead to increased variance and overfitting. Our empirical study shows that, for many data sets, there is an advantage to weighting features, but that increasing the number of possible weights beyond two (zero and one) has very little benefit and sometimes degrades performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #40922. 	[Title] An Optimal Weighting Criterion of Case Indexing for Both Numeric and Symbolic Attributes [Abstract] Indexing of cases is an important topic for Memory-Based Reasoning(MBR). One key problem is how to assign weights to attributes of cases. Although several weighting methods have been proposed, some methods cannot handle numeric attributes directly, so it is necessary to discretize numeric values by classification. Furthermore, existing methods have no theoretical background, so little can be said about optimality. We propose a new weighting method based on a statistical technique called Quantification Method II. It can handle both numeric and symbolic attributes in the same framework. Generated attribute weights are optimal in the sense that they maximize the ratio of variance between classes to variance of all cases. Experiments on several benchmark tests show that in many cases, our method obtains higher accuracies than some other weighting methods. The results also indicate that it can distinguish relevant attributes from irrelevant ones, and can tolerate noisy data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39130. 	[Title] Average-Case Analysis of a Nearest Neighbor Algorithm [Abstract] In this paper we present an average-case analysis of the nearest neighbor algorithm, a simple induction method that has been studied by many researchers. Our analysis assumes a conjunctive target concept, noise-free Boolean attributes, and a uniform distribution over the instance space. We calculate the probability that the algorithm will encounter a test instance that is distance d from the prototype of the concept, along with the probability that the nearest stored training case is distance e from this test instance. From this we compute the probability of correct classification as a function of the number of observed training cases, the number of relevant attributes, and the number of irrelevant attributes. We also explore the behavioral implications of the analysis by presenting predicted learning curves for artificial domains, and give experimental results on these domains as a check on our reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521183. 	[Title] Formalising the knowledge content of case memory systems [Abstract] Discussions of case-based reasoning often reflect an implicit assumption that a case memory system will become better informed, i.e. will increase in knowledge, as more cases are added to the case-base. This paper considers formalisations of this `knowledge content' which are a necessary preliminary to more rigourous analysis of the performance of case-based reasoning systems. In particular we are interested in modelling the learning aspects of case-based reasoning in order to study how the performance of a case-based reasoning system changes as it accumlates problem-solving experience. The current paper presents a `case-base semantics' which generalises recent formalisations of case-based classification. Within this framework, the paper explores various issues in assuring that these sematics are well-defined, and illustrates how the knowledge content of the case memory system can be seen to reside in both the chosen similarity measure and in the cases of the case-base. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #654177. 	[Title] RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING [Abstract] The need for software modules performing natural language processing (NLP) tasks is growing. These modules should perform efficiently and accurately, while at the same time rapid development is often mandatory. Recent work has indicated that machine learning techniques in general, and memory-based learning (MBL) in particular, offer the tools to meet both ends. We present examples of modules trained with MBL on three NLP tasks: (i) text-to-speech conversion, (ii) part-of-speech tagging, and (iii) phrase chunking. We demonstrate that the three modules display high generalization accuracy, and argue why MBL is applicable similarly well to a large class of other NLP tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #120039. 	[Title] EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS [Abstract] The relative performance of different methods for classifier learning varies across domains. Some recent Instance Based Learning (IBL) methods, such as IB1-MVDM* 10 , use similarity measures based on conditional class probabilities. These probabilities are a key component of Naive Bayes methods. Given this commonality of approach, it is of interest to consider how the differences between the two methods are linked to their relative performance in different domains. Here we interpret Naive Bayes in an IBL like framework, identifying differences between Naive Bayes and IB1-MVDM* in this framework. Experiments on variants of IB1-MVDM* that lie between it and Naive Bayes in the framework are conducted on sixteen domains. The results strongly suggest that the relative performance of Naive Bayes and IB1-MVDM* is linked to the extent to which each class can be satisfactorily represented by a single instance in the IBL framework. However, this is not the only factor that appears significant. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #429805. 	[Title] Resolving PP attachment Ambiguities with Memory-Based Learning [Abstract] In this paper we describe the application of Memory-Based Learning to the problem of Prepositional Phrase attachment disambiguation. We compare Memory-Based Learning, which stores examples in memory and generalizes by using intelligent similarity metrics, with a number of recently proposed statistical methods that are well suited to large numbers of features. We evaluate our methods on a common benchmark dataset and show that our method compares favorably to previous methods, and is well-suited to incorporating various unconventional representations of word patterns such as value difference metrics and Lexical Space. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128430. 	[Title] On Reasoning from Data [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #167670. 	[Title] Error-Based and Entropy-Based Discretization of Continuous Features [Abstract] We present a comparison of error-based and entropy-based methods for discretization of continuous features. Our study includes both an extensive empirical comparison as well as an analysis of scenarios where error minimization may be an inappropriate discretization criterion. We present a discretization method based on the C4.5 decision tree algorithm and compare it to an existing entropy-based discretization algorithm, which employs the Minimum Description Length Principle, and a recently proposed error-based technique. We evaluate these discretization methods with respect to C4.5 and Naive-Bayesian classifiers on datasets from the UCI repository and analyze the computational complexity of each method. Our results indicate that the entropy-based MDL heuristic outperforms error minimization on average. We then analyze the shortcomings of error-based approaches in comparison to entropy-based methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521207. 	[Title] A Formal Analysis of Case Base Retrieval [Abstract] Case based systems typically retrieve cases from the case base by applying similarity measures. The measures are usually constructed in an ad hoc manner. This report presents a toolbox for the systematic construction of similarity measures. In addition to paving the way to a design methodology for similarity measures, this systematic approach facilitates the identification of opportunities for parallelisation in case base retrieval. Please summarise all the given information.

Paper #1125992 has the title `Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the
` and the abstract `The effectiveness of a case-based reasoning system is known to depend critically on its similarity measure. However, it is not clear whether there are elusive and esoteric similarity measures which might improve the performance of a case-based reasoner if substituted for the more commonly used measures. This paper therefore deals with the problem of choosing the best similarity measure, in the limited context of instance-based learning of classifications of a discrete example space. We consider both `fixed' similarity measures and `learnt' ones. In the former case, we give a definition of a similarity measure which we believe to be `optimal' w.r.t. the current prior distribution of target concepts and prove its optimality within a restricted class of similarity measures. We then show how this `optimal' similarity measure is instantiated by some specific prior distributions, and conclude that a very simple similarity measure is as good as any other in these cases. In a further section, we then show how our definition leads naturally to a conjecture about the 
`.
Question: Which category should Paper #1125992 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1125992 has the title `Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the
` and the abstract `The effectiveness of a case-based reasoning system is known to depend critically on its similarity measure. However, it is not clear whether there are elusive and esoteric similarity measures which might improve the performance of a case-based reasoner if substituted for the more commonly used measures. This paper therefore deals with the problem of choosing the best similarity measure, in the limited context of instance-based learning of classifications of a discrete example space. We consider both `fixed' similarity measures and `learnt' ones. In the former case, we give a definition of a similarity measure which we believe to be `optimal' w.r.t. the current prior distribution of target concepts and prove its optimality within a restricted class of similarity measures. We then show how this `optimal' similarity measure is instantiated by some specific prior distributions, and conclude that a very simple similarity measure is as good as any other in these cases. In a further section, we then show how our definition leads naturally to a conjecture about the 
`.
Question: Which category should Paper #1125992 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: Case_Based