Learning classifier systems international workshops IWLCS 2003 2005 1st Edition by Jaume Bacardit, Ester BernadoMansilla, Martin V Butz ISBN 9783540881377 by Tim Kovacs 3540712305 instant download after payment.

Learning classifier systems international workshops IWLCS 2003 2005 1st Edition by Jaume Bacardit, Ester BernadoMansilla, Martin V Butz - Ebook PDF Instant Download/Delivery: 9783540881377
Full download Learning classifier systems international workshops IWLCS 2003 2005 1st Edition after payment

Product details:

ISBN 13: 9783540881377
Author: Jaume Bacardit, Ester BernadoMansilla, Martin V Butz

This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.

Learning classifier systems international workshops IWLCS 2003 2005 1st Table of contents:

1. LCSs: Types and Approaches
2. Basic LCS Components
3. Michigan vs. Pittsburgh LCSs
4. Recent Advances in LCSs
5. Condition Structure
6. Action Structures
7. Prediction Structure
8. Classifier Competition
9. Rule Structure Evolution Mechanisms
10. Theory and Robustness
11. Interpretability and Compaction
12. Efficiency Enhancement Techniques
13. Applications
14. Cognitive Systems
15. Challenges and Opportunities
16. Problem Structure and LCS Modules
17. LCS Cookbook
18. Data Mining
19. Conclusions
20. References
21. Knowledge Representations
22. Analysis of Population Evolution in Classifier Systems Using Symbolic Representations
23. Introduction
24. Subexpressions Extraction
25. The Canonical Form
26. Population Simplification
27. Extraction of Subexpressions
28. Subexpressions Counting
29. Analysis of Evolving Populations
30. Experimental Validation
31. Experiment 1: Sum of Two Variables
32. Experiment 2: Multiplication of Two Variables
33. Summary
34. References
35. Investigating Scaling of an Abstracted LCS Utilising Ternary and S-Expression Alphabets
36. Introduction
37. Background
38. Design of the S-XCS System
39. Results
40. Discussion
41. Conclusions
42. References
43. Evolving Fuzzy Rules with UCS: Preliminary Results
44. Introduction
45. Description of Fuzzy-UCS
46. Representation
47. Performance Component
48. Parameters Update
49. Discovery Component
50. Fuzzy-UCS in Test Mode
51. Experimentation
52. Methodology
53. Results
54. Conclusions and Further Work
55. References
56. Analysis of the System
57. A Principled Foundation for LCS
58. Introduction
59. Assembling an LCS Model
60. A Bird’s Eye View of the LCS Model
61. Mixtures of Experts
62. LCS as Generalised Mixtures of Experts
63. Training the Classifiers Independently
64. Finding a Good Set of Classifiers
65. Applying Bayesian Model Selection
66. A Bayesian LCS Model
67. Evaluating Posterior and Model Evidence
68. Summarising the Approach
69. But..., Does it Work?
70. Model Structure Search
71. Approximating a Generated Function
72. Variable Measurement Noise
73. Summary and Conclusions
74. References
75. Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS
76. Introduction
77. XCSinaNutshell
78. Description of UCS
79. UCS Components
80. Why Should We Not Share Fitness?
81. XCS and UCS in Binary-Input Problems
82. Methodology
83. Binary-Class Problem: Parity
84. Multiclass Problem: Decoder
85. Imbalanced Binary-Class Problem: Imbalanced Multiplexer
86. Imbalanced Multiclass Problem: Position
87. Noisy Problem: Multiplexer with Alternating Noise
88. Summing Up
89. Conclusions
90. References
91. Mechanisms
92. Analysis and Improvements of the Classifier Error Estimate in XCSF
93. Introduction
94. The XCSF Classifier System
95. Squared Error or Absolute Error?
96. Re-deriving the XCSF Weight Vector and Error Update
97. Estimating the Root Mean Squared Error
98. Improving the Error Estimate
99. The Bayes Linear Analysis
100. A Sample-Based Implementation and Its Relation to Least Squares
101. Recursive Least Squares and Error Tracking
102. Experimental Design
103. Experimental Results
104. Single Classifier Error
105. Analysis of Generalization
106. Classifier Error and Action-Set Subsumption
107. Conclusions
108. References
109. A Learning Classifier System with Mutual-Information-Based Fitness
110. Introduction
111. LCSs and CCNs: An Analogy
112. Parameter Versus Structural Learning
113. CCN and Structural Learning
114. CCN and XCS
115. Supervised Versus Reinforcement Learning
116. The Role of Mutual Information in MILCS
117. The MILCS Process
118. Results
119. Multiplexer Problems
120. Scalability
121. Explanatory Power
122. Visualization of Explanatory Power
123. Coordination Number Problem
124. Final Comments and Future Directions
125. References
126. On Lookahead and Latent Learning in Simple LCS
127. Introduction
128. MCSL: A Simple Anticipatory Classifier System
129. A Simple Model of MCSL
130. MCSL in T-Mazes
131. Self-adaptive Mutation
132. Conclusions
133. References
134. A Learning Classifier System Approach to Relational Reinforcement Learning
135. Introduction
136. System Design
137. Representation
138. Matching
139. Rule Discovery
140. Evaluation
141. Comparison to ILP Algorithms
142. Relational Reinforcement Learning
143. Learning Scalable Policies
144. Conclusion
145. References
146. Linkage Learning, Rule Representation, and the {large $chi$}-Ary Extended Compact Classifier Syste
147. Introduction
148. The {lowercase{{large $chi$}-Ary}} Extended Compact Classifier System
149. Restricted Tournament Replacement
150. Results
151. Substructure in the Multiplexer
152. Getting a Set of Rules
153. Probabilistic Models and Knowledge Representations
154. Gene Expression Programming and the Karva Language
155. Building Probabilistic Models for the Karva Language
156. Rule Representation, Probabilistic Model Building, and Population Sizes
157. Conclusions
158. References
159. New Directions
160. Classifier Conditions Using Gene Expression Programming
161. Introduction
162. Limits of Traditional Conditions
163. Gene Expression Programming in XCSF
164. Some Basics of GEP
165. XCSF-GEP
166. An Experiment
167. Setup
168. Results
169. Discussion and Conclusion
170. References
171. Evolving Classifiers Ensembles with Heterogeneous Predictors
172. Introduction
173. The XCSF Classifier System
174. Classifiers
175. Performance Component
176. Reinforcement Component
177. Discovery Component
178. XCSF with Heterogeneous Predictors
179. Covering Operator
180. Discovery Component
181. Predictor Ensembles
182. XCSFHP for Function Approximation
183. Experiments with Polynomial Predictors
184. Experiments with Constant, Linear and Neural Predictors
185. XCSFHPonMultistepProblems
186. 2D Continuous Gridworld
187. 2D Continuous Gridworld with Puddles
188. Conclusions
189. References
190. Substructural Surrogates for Learning Decomposable Classification Problems
191. Introduction
192. Methodology for Learning $chi$-Ary Input Problems
193. Structural Model Layer
194. Surrogate Model Layer
195. Classification Model Layer
196. Implementing the Methodology: gESMC
197. Test Problems
198. Lower Level of the Hierarchy
199. Higher Level of the Hierarchy
200. Results
201. Experimental Methodology
202. Results with 2-Bit Low Order Blocks
203. Results Increasing the Low Level Block Size
204. Discussion
205. Lack of Guidance from Lower-Order Substructures
206. Non-linearities in the High Order Functions
207. Creating Structural Models with Overlapping Substructures
208. Summary and Conclusions
209. References
210. Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
211. Introduction
212. Related Work
213. The GAssist Learning Classifier System
214. Ensembles for Consensus Prediction
215. Empirical Evaluation
216. Ensembles for Ordinal Classification
217. Motivation
218. Ensemble Definition
219. Empirical Evaluation of the Hierarchical Ensemble
220. Conclusions and Further Work
221. References
222. Applications
223. Technology Extraction of Expert Operator Skills from Process Time Series Data
224. Introduction
225. Research Objective
226. A Target Plant
227. Problem Description
228. Principles of LCS with MDL
229. MDL Criteria
230. Improvement Rate Based MDL Criteria
231. Learning Classifier System
232. Comparison between MDL and iMDL
233. Experiments
234. The Response Model
235. Heuristic Search for Operation Rules
236. Comparison with Conventional Methods
237. Extracting Knowledge of Workflow from Workers
238. Detection of Outliers
239. Conclusion
240. References
241. Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks
242. Introduction
243. Learning Classifier Systems Fundamentals
244. LCS
245. XCS

People also search for Learning classifier systems international workshops IWLCS 2003 2005 1st:

a sequential algorithm for training text classifiers
        
    
machine learning workshops
        
    
training classifiers
        
    
classifier training
        
    
dx learning care
        
    
elearning conferences 2022
        
    
elearning conferences 2023

Tags: Jaume Bacardit, Ester BernadoMansilla, Martin V Butz, systems, international