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Design and Analysis of Learning Classifier Systems : A Probabilistic Approach /

By: Drugowitsch, Jan [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 139Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: XIV, 267 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540798668.Subject(s): Computer science | Artificial intelligence | Applied mathematics | Engineering mathematics | Computer Science | Artificial Intelligence (incl. Robotics) | Appl.Mathematics/Computational Methods of EngineeringDDC classification: 006.3 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
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Item type Current location Call number Status Date due Barcode Item holds
E books E books PK Kelkar Library, IIT Kanpur
Available EBK928
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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.

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.

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