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Synopsis
This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. The text builds an understanding from basic ideas and concepts. The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. The applicability and adaptability of these methods is highlighted by providing descriptions of common methodological alternatives...
About the Author
Ryan Urbanowicz is a postdoctoral research associate in the Dept. of Biostatistics, Epidemiology, and Informatics in the Perelman School of Medicine at the University of Pennsylvania. He received his PhD in Genetics from Dartmouth College, and a B.S. and M.Eng. in Biological Engineering from Cornell University. His areas of research include bioinformatics, data mining, machine learning, evolutionary algorithms, learning classifier systems, data visualization, and epidemiology. He has cochaired...
Review
“Introduction to Learning Classifier Systems is an excellent textbook and introduction to Learning Classifier Systems. ... The book is completed with Python code available through a link included in the book. ... Urbanowicz and Browne recommend their book for undergraduate and postgraduate students, data analysts, and machine learning practitioners alike.” (Analía Amandi, Genetic Programming and Evolvable Machines, Vol. 19 (4), December, 2018)
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date2017
- ISBN 10 3662550067
- ISBN 13 9783662550069
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages136
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Introduction to Learning Classifier Systems
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Springer, Berlin, Springer Berlin Heidelberg, Springer, 2017
ISBN 10: 3662550067
ISBN 13: 9783662550069
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. The text builds an understanding from basic ideas and concepts. The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. The applicability and adaptability of these methods is highlighted by providing descriptions of common methodological alternatives for different components that are suited to different types of problems from data mining to autonomous robotics. The authors have also paired exercises and a simple educational LCS (eLCS) algorithm (implemented in Python) with this book. It is suitable for courses or self-study by advanced undergraduate and postgraduate students in subjects such as Computer Science, Engineering, Bioinformatics, and Cybernetics, and by researchers, data analysts, andmachine learning practitioners. 123 pp. Englisch. Seller Inventory # 9783662550069
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Introduction to Learning Classifier Systems
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Introduction to Learning Classifier Systems
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ISBN 10: 3662550067
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. The text builds an understanding from basic ideas and concepts. The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. The applicability and adaptability of these methods is highlighted by providing descriptions of common methodological alternatives for different components that are suited to different types of problems from data mining to autonomous robotics. The authors have also paired exercises and a simple educational LCS (eLCS) algorithm (implemented in Python) with this book. It is suitable for courses or self-study by advanced undergraduate and postgraduate students in subjects such as Computer Science, Engineering, Bioinformatics, and Cybernetics, and by researchers, data analysts, andmachine learning practitioners. Seller Inventory # 9783662550069
Quantity: 2 available