For the last few decades, machine learning has been the province of a few enthusiasts. Like other forms of artificial intelligence, it held great promise but remained largely undeveloped. This situation is changing. We are now familiar with a wide range of algorithms, and a theory outlining which algorithm will suit which purpose is beginning to emerge. The science of machine learning is coming of age. Algorithmic Learning provides a thorough introduction to all aspects of the subject. It presents more than 30 algorithms as well as examples, exercises, and comparisons between underlying methods. The last chapter summarizes several approaches to learning theory and discusses representations, bias, and other topics of current research. The book will be invaluable to students, researchers, and professionals in artificial intelligence, neural computing, and cognitive science seeking an up-to-date review of this exciting and dynamic field.
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Table of Contents
1. Characteristics of learning algorithms