Introduction to Learning Classifier Systems (SpringerBriefs in Intelligent Systems)
| Author | : | |
| Rating | : | 4.67 (525 Votes) |
| Asin | : | 3662550067 |
| Format Type | : | paperback |
| Number of Pages | : | 122 Pages |
| Publish Date | : | 2013-06-20 |
| Language | : | English |
DESCRIPTION:
and M.Eng. His areas of research include bioinformatics, data mining, machine learning, evolutionary algorithms, learning classifier systems, data visualization, and epidemiology. He has cochaired the Intl. from Cardiff University. of Biostatistics, Epidemiology, and Informatics in the Perelman School of Medicine at the University of Pennsylvania. His main area of research is applied cognitive systems, in particular cognitive robotics, Learning Classifier Systems (LCSs), and modern heuristics fo
The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. 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.. The text builds an understanding from basic ideas and concepts. 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. From the Back CoverThis accessible introduction shows the reader h
The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. The text builds an understanding from basic ideas and concepts. 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. This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. 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.