Learning Classifier Systems (Record no. 182443)

MARC details
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fixed length control field 05862nam a22006375i 4500
001 - CONTROL NUMBER
control field 978-3-540-71231-2
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423125846.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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fixed length control field 100301s2007 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783540712312
-- 978-3-540-71231-2
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-540-71231-2
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q334-342
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA347.A78
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM004000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
245 10 - TITLE STATEMENT
Title Learning Classifier Systems
Medium [electronic resource] :
Remainder of title International Workshops, IWLCS 2003-2005, Revised Selected Papers /
Statement of responsibility, etc edited by Tim Kovacs, Xavier LlorĂ , Keiki Takadama, Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2007.
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg :
-- Imprint: Springer,
-- 2007.
300 ## - PHYSICAL DESCRIPTION
Extent XII, 345 p.
Other physical details online resource.
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-- online resource
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490 1# - SERIES STATEMENT
Series statement Lecture Notes in Artificial Intelligence,
International Standard Serial Number 2945-9141 ;
Volume number/sequential designation 4399
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Knowledge Representation -- Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS -- Use of Learning Classifier System for Inferring Natural Language Grammar -- Backpropagation in Accuracy-Based Neural Learning Classifier Systems -- Binary Rule Encoding Schemes: A Study Using the Compact Classifier System -- Mechanisms -- Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System -- Post-processing Clustering to Decrease Variability in XCS Induced Rulesets -- LCSE: Learning Classifier System Ensemble for Incremental Medical Instances -- Effect of Pure Error-Based Fitness in XCS -- A Fuzzy System to Control Exploration Rate in XCS -- Counter Example for Q-Bucket-Brigade Under Prediction Problem -- An Experimental Comparison Between ATNoSFERES and ACS -- The Class Imbalance Problem in UCS Classifier System: A Preliminary Study -- Three Methods for Covering Missing Input Data in XCS -- New Directions -- A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients -- Adaptive Value Function Approximations in Classifier Systems -- Three Architectures for Continuous Action -- A Formal Relationship Between Ant Colony Optimizers and Classifier Systems -- Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis -- Application-Oriented Research and Tools -- Data Mining in Learning Classifier Systems: Comparing XCS with GAssist -- Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule -- Using XCS to Describe Continuous-Valued Problem Spaces -- The EpiXCS Workbench: A Tool for Experimentation and Visualization.
520 ## - SUMMARY, ETC.
Summary, etc The work embodied in this volume was presented across three consecutive e- tions of the International Workshop on Learning Classi?er Systems that took place in Chicago (2003), Seattle (2004), and Washington (2005). The Genetic and Evolutionary Computation Conference, the main ACM SIGEvo conference, hosted these three editions. The topics presented in this volume summarize the wide spectrum of interests of the Learning Classi?er Systems (LCS) community. The topics range from theoretical analysis of mechanisms to practical cons- eration for successful application of such techniques to everyday data-mining tasks. When we started editing this volume, we faced the choice of organizing the contents in a purely chronologicalfashion or as a sequence of related topics that help walk the reader across the di?erent areas. In the end we decided to or- nize the contents by area, breaking the time-line a little. This is not a simple endeavor as we can organize the material using multiple criteria. The tax- omy below is our humble e?ort to provide a coherent grouping. Needless to say, some works may fall in more than one category. The four areas are as follows: Knowledge representation. These chapters elaborate on the knowledge r- resentations used in LCS. Knowledge representation is a key issue in any learning system and has implications for what it is possible to learn and what mechanisms shouldbe used. Four chapters analyze di?erent knowledge representations and the LCS methods used to manipulate them.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine theory.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Theory of Computation.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Formal Languages and Automata Theory.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining and Knowledge Discovery.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Kovacs, Tim.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name LlorĂ , Xavier.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Takadama, Keiki.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Lanzi, Pier Luca.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Stolzmann, Wolfgang.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Wilson, Stewart W.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783540712305
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783540836056
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Lecture Notes in Artificial Intelligence,
-- 2945-9141 ;
Volume number/sequential designation 4399
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-540-71231-2">https://doi.org/10.1007/978-3-540-71231-2</a>
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Koha item type eBooks-CSE-Springer

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