Design and Analysis of Learning Classifier Systems: A Probabilistic Approach: Studies in Computational Intelligence, cartea 139
Autor Jan Drugowitschen Limba Engleză Hardback – 30 mai 2008
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Specificații
ISBN-13: 9783540798651
ISBN-10: 354079865X
Pagini: 284
Ilustrații: XIV, 267 p.
Dimensiuni: 156 x 234 x 20 mm
Greutate: 0.58 kg
Ediția:2008
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 354079865X
Pagini: 284
Ilustrații: XIV, 267 p.
Dimensiuni: 156 x 234 x 20 mm
Greutate: 0.58 kg
Ediția:2008
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Background.- A Learning Classifier Systems Model.- A Probabilistic Model for LCS.- Training the Classifiers.- Mixing Independently Trained Classifiers.- The Optimal Set of Classifiers.- An Algorithmic Description.- Towards Reinforcement Learning with LCS.- Concluding Remarks.
Textul de pe ultima copertă
This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems. Contrary to commonly approaching their design and analysis from the viewpoint of evolutionary computation, this book instead promotes a probabilistic model-based approach, based on their defining question "What is an LCS supposed to learn?". Systematically following this approach, it is shown how generic machine learning methods can be applied to design LCS algorithms from the first principles of their underlying probabilistic model, which is in this book -- for illustrative purposes -- closely related to the currently prominent XCS classifier system. The approach is holistic in the sense that the uniform goal-driven design metaphor essentially covers all aspects of LCS and puts them on a solid foundation, in addition to enabling the transfer of the theoretical foundation of the various applied machine learning methods onto LCS. Thus, it does not only advance the analysis of existing LCS but also puts forward the design of new LCS within that same framework.
Caracteristici
Latest research in the area of Learning Classifier Systems Presents a probabilistic approach to Design and Analysis of Learning Classifier Systems