Ebook: Lazy Learning

This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies. It is the first edited volume in AI on this topic, whose many synonyms include `instance-based', `memory-based'. `exemplar-based', and `local learning', and whose topic intersects case-based reasoning and edited k-nearest neighbor classifiers. It is intended for AI researchers and students interested in pursuing recent progress in this branch of machine learning, but, due to the breadth of its contributions, it should also interest researchers and practitioners of data mining, case-based reasoning, statistics, and pattern recognition.

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This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies. It is the first edited volume in AI on this topic, whose many synonyms include `instance-based', `memory-based'. `exemplar-based', and `local learning', and whose topic intersects case-based reasoning and edited k-nearest neighbor classifiers. It is intended for AI researchers and students interested in pursuing recent progress in this branch of machine learning, but, due to the breadth of its contributions, it should also interest researchers and practitioners of data mining, case-based reasoning, statistics, and pattern recognition.

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This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies. It is the first edited volume in AI on this topic, whose many synonyms include `instance-based', `memory-based'. `exemplar-based', and `local learning', and whose topic intersects case-based reasoning and edited k-nearest neighbor classifiers. It is intended for AI researchers and students interested in pursuing recent progress in this branch of machine learning, but, due to the breadth of its contributions, it should also interest researchers and practitioners of data mining, case-based reasoning, statistics, and pattern recognition.
Content:
Front Matter....Pages i-6
Editorial....Pages 7-10
Locally Weighted Learning....Pages 11-73
Locally Weighted Learning for Control....Pages 75-113
Voting over Multiple Condensed Nearest Neighbors....Pages 115-132
Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching....Pages 133-155
Discretisation in Lazy Learning Algorithms....Pages 157-174
Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms....Pages 175-191
The Racing Algorithm: Model Selection for Lazy Learners....Pages 193-225
Context-Sensitive Feature Selection for Lazy Learners....Pages 227-253
Computing Optimal Attribute Weight Settings for Nearest Neighbor Algorithms....Pages 255-272
A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms....Pages 273-314
Lazy Acquisition of Place Knowledge....Pages 315-342
A Teaching Strategy for Memory-Based Control....Pages 343-370
Lazy Incremental Learning of Control Knowledge for Efficiently Obtaining Quality Plans....Pages 371-405
IGTree: Using Trees for Compression and Classification in Lazy Learning Algorithms....Pages 407-423

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This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies. It is the first edited volume in AI on this topic, whose many synonyms include `instance-based', `memory-based'. `exemplar-based', and `local learning', and whose topic intersects case-based reasoning and edited k-nearest neighbor classifiers. It is intended for AI researchers and students interested in pursuing recent progress in this branch of machine learning, but, due to the breadth of its contributions, it should also interest researchers and practitioners of data mining, case-based reasoning, statistics, and pattern recognition.
Content:
Front Matter....Pages i-6
Editorial....Pages 7-10
Locally Weighted Learning....Pages 11-73
Locally Weighted Learning for Control....Pages 75-113
Voting over Multiple Condensed Nearest Neighbors....Pages 115-132
Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching....Pages 133-155
Discretisation in Lazy Learning Algorithms....Pages 157-174
Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms....Pages 175-191
The Racing Algorithm: Model Selection for Lazy Learners....Pages 193-225
Context-Sensitive Feature Selection for Lazy Learners....Pages 227-253
Computing Optimal Attribute Weight Settings for Nearest Neighbor Algorithms....Pages 255-272
A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms....Pages 273-314
Lazy Acquisition of Place Knowledge....Pages 315-342
A Teaching Strategy for Memory-Based Control....Pages 343-370
Lazy Incremental Learning of Control Knowledge for Efficiently Obtaining Quality Plans....Pages 371-405
IGTree: Using Trees for Compression and Classification in Lazy Learning Algorithms....Pages 407-423
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