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Design and Analysis of Learning Classifier Systems A Probabilistic Approach /
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 sp...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edición: | 1st ed. 2008. |
| Colección: | Studies in Computational Intelligence,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 024 | 7 | |a 10.1007/978-3-540-79866-8 |2 doi | |
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| 100 | 1 | |a Drugowitsch, Jan. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Design and Analysis of Learning Classifier Systems |h [electronic resource] : |b A Probabilistic Approach / |c by Jan Drugowitsch. |
| 250 | |a 1st ed. 2008. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2008. | |
| 300 | |a XIV, 267 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Studies in Computational Intelligence, |x 1860-9503 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Engineering mathematics. | |
| 650 | 0 | |a Engineering-Data processing. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Mathematical and Computational Engineering Applications. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642098611 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540872771 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540798651 |
| 830 | 0 | |a Studies in Computational Intelligence, |x 1860-9503 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-540-79866-8 |z Texto Completo |
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| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||