Urbanowicz*Introduction to Learning Classifier


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. This accessible introduction shows the reader how to understand, implement, adapt, and apply 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 concepts and components of LCS that form this rule-based evolutionary algorithm. They examine each important method or component, with common alternatives, to demonstrate applicability and they explain how to set up an LCS for a given problem, with examples from data mining to autonomous robotics.

The authors paired the educational LCS (eLCS) algorithm, implemented in Python, with exercises in this book, and 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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