Learning with Nested Generalized Exemplars / Edition 1

Learning with Nested Generalized Exemplars / Edition 1

by Steven L. Salzberg
     
 

ISBN-10: 0792391101

ISBN-13: 9780792391104

Pub. Date: 05/31/1990

Publisher: Springer US

Product Details

ISBN-13:
9780792391104
Publisher:
Springer US
Publication date:
05/31/1990
Series:
The Springer International Series in Engineering and Computer Science, #100
Edition description:
1990
Pages:
160
Product dimensions:
9.21(w) x 6.14(h) x 0.50(d)

Table of Contents

1 Introduction.- 1.1 Background.- 1.2 NGE and other exemplar-based theories.- 1.3 Previous models.- 1.3.1 Concept learning.- 1.3.2 Explanation-based generalization.- 1.4 Comparisons of NGE and other models.- 1.4.1 Knowledge representation schemes.- 1.4.2 Underlying learning strategies.- 1.4.3 External information.- 1.4.4 Domain independent learning.- 1.4.5 Generalizations with exceptions.- 1.4.6 One-shot learning.- 1.4.7 Many variables, many concepts.- 1.4.8 Binary, discrete, and continuous variables.- 1.4.9 Disjunctive concepts.- 1.4.10 Inconsistent data.- 1.4.11 Psychological credibility.- 1.4.12 Problem domain characteristics.- 1.5 Types of generalization.- 1.5.1 Implicit generalization.- 1.5.2 Explicit generalization.- 2 The NGE learning algorithm.- 2.1 Initialization.- 2.2 Get the next example.- 2.3 Make a prediction.- 2.3.1 The matching process.- 2.4 Feedback.- 2.4.1 Correct prediction.- 2.4.2 Incorrect prediction.- 2.5 Summary of algorithm.- 2.6 Partitioning feature space.- 2.6.1 Simplest case.- 2.6.2 Two rectangles in two dimensions.- 2.7 Assumptions.- 2.8 Greedy variant of the algorithm.- 3 Review.- 3.1 Concept learning in psychology.- 3.1.1 Spectator behavior.- 3.1.2 Participant behavior.- 3.2 Prototype theory and exemplar theory.- 3.3 Each as a multiple prototype model.- 3.4 Machine learning in AI.- 3.4.1 Learning by discovery.- 3.4.2 Natural language learning.- 3.4.3 Earlier work on nested generalizations.- 3.4.4 Other exemplar-based learning models.- 3.5 Connectionism.- 3.5.1 Simulations.- 3.6 Cluster analysis.- 3.6.1 Similarity measures.- 3.6.2 Hierarchical clustering techniques.- 3.6.3 Problems with cluster analysis.- 3.7 Conclusion.- 4 Experimental results with NGE.- 4.1 Breast cancer data.- 4.1.1 Success rates and comparisons.- 4.1.2 Variability.- 4.1.3 Memory size.- 4.1.4 Other tests.- 4.2 Iris classification.- 4.2.1 Success rates.- 4.2.2 Memory size and variability.- 4.2.3 Learning rate.- 4.2.4 Comparisons.- 4.2.5 Memory size and structure.- 4.3 Echocardiogram tests.- 4.3.1 Description of the domain.- 4.3.2 Results and discussion.- 4.3.3 Structure of memory model.- 4.3.4 Greedy Each algorithm results.- 4.4 Discrete event simulation.- 4.4.1 Tests with four variables.- 4.4.2 Tests with five variables.- 4.4.3 Tests with seven variables.- 4.4.4 Summary of simulation.- 5 Conclusion.- 5.1 Weight factors.- 5.2 Synthesis with explanation-based learning.- 5.3 Psychological plausibility.- 5.4 Complexity results.- 5.5 Future experimental work.- A Data sets.- A.1 Breast cancer data.- A.2 Iris data.- A.3 Echocardiogram data.

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