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Learning Classifier Systems

Overview of attention for book
Cover of 'Learning Classifier Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS
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    Chapter 2 Use of Learning Classifier System for Inferring Natural Language Grammar
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    Chapter 3 Backpropagation in Accuracy-Based Neural Learning Classifier Systems
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    Chapter 4 Binary Rule Encoding Schemes: A Study Using the Compact Classifier System
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    Chapter 5 Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System
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    Chapter 6 Post-processing Clustering to Decrease Variability in XCS Induced Rulesets
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    Chapter 7 LCSE: Learning Classifier System Ensemble for Incremental Medical Instances
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    Chapter 8 Effect of Pure Error-Based Fitness in XCS
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    Chapter 9 A Fuzzy System to Control Exploration Rate in XCS
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    Chapter 10 Counter Example for Q-Bucket-Brigade Under Prediction Problem
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    Chapter 11 An Experimental Comparison Between ATNoSFERES and ACS
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    Chapter 12 The Class Imbalance Problem in UCS Classifier System: A Preliminary Study
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    Chapter 13 Three Methods for Covering Missing Input Data in XCS
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    Chapter 14 A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients
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    Chapter 15 Adaptive Value Function Approximations in Classifier Systems
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    Chapter 16 Three Architectures for Continuous Action
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    Chapter 17 A Formal Relationship Between Ant Colony Optimizers and Classifier Systems
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    Chapter 18 Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis
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    Chapter 19 Data Mining in Learning Classifier Systems: Comparing XCS with GAssist
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    Chapter 20 Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule
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    Chapter 21 Using XCS to Describe Continuous-Valued Problem Spaces
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    Chapter 22 The EpiXCS Workbench: A Tool for Experimentation and Visualization
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Mentioned by

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2 Wikipedia pages

Citations

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3 Dimensions

Readers on

mendeley
4 Mendeley
Summary Wikipedia Dimensions citations
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Title
Learning Classifier Systems
Published by
Springer Berlin Heidelberg, January 2007
DOI 10.1007/978-3-540-71231-2
ISBNs
978-3-54-071230-5, 978-3-54-071231-2
Editors

Tim Kovacs, Xavier Llorà, Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 25%
Student > Ph. D. Student 1 25%
Unknown 2 50%
Readers by discipline Count As %
Computer Science 1 25%
Engineering 1 25%
Unknown 2 50%

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