Above are papers related to the following paper: [Paper_ID] Paper #60682. 	[Title] ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts [Abstract] In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently, a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning, and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected. In order to efficiently accommodate changing contexts, a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modification of the attribute set used to describe instances in a problem domain. We have implemented the capability in a concept learning system that has been evaluated along several dimensions using an existing concept formation system for com parison. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #14531. 	[Title] Context-Sensitive Feature Selection for Lazy Learners [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56167. 	[Title] Refining Conversational Case Libraries [Abstract] Conversational case-based reasoning (CBR) shells (e.g., Inference's CBR Express) are commercially successful tools for supporting the development of help desk and related applications. In contrast to rule-based expert systems, they capture knowledge as cases rather than more problematic rules, and they can be incrementally extended. However, rather than eliminate the knowledge engineering bottleneck, they refocus it on case engineering, the task of carefully authoring cases according to library design guidelines to ensure good performance. Designing complex libraries according to these guidelines is difficult; software is needed to assist users with case authoring. We describe an approach for revising case libraries according to design guidelines, its implementation in Clire, and empirical results showing that, under some conditions, this approach can improve conversational CBR performance. Please summarise all the given information.

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Above are papers related to the following paper: [Paper_ID] Paper #1152308. 	[Title] Learning to Refine Case Libraries: [Abstract] Initial Results Abstract. Conversational case-based reasoning (CBR) systems, which incrementally extract a query description through a user-directed conversation, are advertised for their ease of use. However, designing large case libraries that have good performance (i.e., precision and querying efficiency) is difficult. CBR vendors provide guidelines for designing these libraries manually, but the guidelines are difficult to apply. We describe an automated inductive approach that revises conversational case libraries to increase their conformance with design guidelines. Revision increased performance on three conversational case libraries. 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 #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 #1103038. 	[Title] The Management of Context-Sensitive Features: A Review of Strategies [Abstract] In this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on context sensitive learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #592830. 	[Title] Context-sensitive attribute estimation in regression [Abstract] One of key issues in both discrete and continuous class prediction and in machine learning in general seems to be the problem of estimating the quality of attributes. Heuristic measures mostly assume independence of attributes so their use is non-optimal in domains with strong dependencies between attributes. For the same reason they are also mostly unable to recognize context dependent features. Relief and its extension Re-liefF are statistical methods capable of correctly estimating the quality of attributes in classification problems with strong dependencies between attributes. By exploiting local information provided by different contexts they provide a global view and recognize contextual attributes. After the analysis of ReliefF we have extended it to continuous class problems. Regressional ReliefF (RReliefF) and ReliefF provide a unified view on estimating attribute quality. The experiments show that RReliefF correctly estimates the quality of attributes, recognizes the contextual attributes and can be used for non myopic learning of the regression trees. 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.

Paper #60682 has the title `ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts  
` and the abstract `In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently, a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning, and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected. In order to efficiently accommodate changing contexts, a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modification of the attribute set used to describe instances in a problem domain. We have implemented the capability in a concept learning system that has been evaluated along several dimensions using an existing concept formation system for com parison.
`.
Question: Which category should Paper #60682 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #60682 has the title `ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts  
` and the abstract `In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently, a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning, and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected. In order to efficiently accommodate changing contexts, a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modification of the attribute set used to describe instances in a problem domain. We have implemented the capability in a concept learning system that has been evaluated along several dimensions using an existing concept formation system for com parison.
`.
Question: Which category should Paper #60682 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