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Learning Classifier Systems (LCSs) are a powerful and well-established rule-based machine learning technique but they have yet to be widely adopted due to a steep learning curve, their rich nature, and a lack of resources, and this is the first accessible introduction
Authors gave related tutorial at key international conference over multiple years
Suitable for undergraduate and postgraduate students, data analysts, and machine learning practitioners
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Über die Autorin bzw. den Autor
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 the Intl. Workshop on Learning Classifier Systems and presented LCS tutorials at GECCO.
Will Browne is an Associate Professor in the School of Engineering and Computer Science of Victoria University of Wellington. He received his Eng.D. from Cardiff University. His main area of research is applied cognitive systems, in particular cognitive robotics, Learning Classifier Systems (LCSs), and modern heuristics for industrial application. He has cochaired the Intl. Workshop on Learning Classifier Systems, and chaired the Genetics-Based Machine Learning track and copresented the LCS tutorial at GECCO.
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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, and machine learning practitioners.
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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. Zustand: 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. Artikel-Nr. 9783662550069
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Introduction to Learning Classifier Systems (SpringerBriefs in Intelligent Systems)
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