Introduction to Learning Classifier Systems (SpringerBriefs in Intelligent Systems)
| Author | : | |
| Rating | : | 4.64 (760 Votes) |
| Asin | : | 3662550067 |
| Format Type | : | paperback |
| Number of Pages | : | 122 Pages |
| Publish Date | : | 2016-09-11 |
| Language | : | English |
DESCRIPTION:
From the Back CoverThis accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. 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 m
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. This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. 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. The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm
and M.Eng. Ryan Urbanowicz is a postdoctoral research associate in the Dept. Workshop on Learning Classifier Systems, and chaired the Genetics-Based Machine Learning track and copresented the LCS tutorial at GECCO.. His main area of research is applied cognitive systems, in particular cognitive robotics, Learning Classifier Systems (LCSs), and modern heuristics for industrial application. Workshop on Learning Classifier Systems and pre