Elements of Machine Learning

by Pat Langley

Recent years have seen an explosion of work on machine learning, the computational study of algorithms that improve performance based on experience. Research on rule induction, neural networks, genetic algorithms, case-based reasoning, and probabilistic inference has produced a variety of robust methods for inducing knowledge from training data. This book covers the main induction algorithms explored in the literature and presents them within a coherent theoretical framework that moves beyond traditional paradigm boundaries.
Elements of Machine Learning provides a comprehensive introduction to the fundamental concepts and problems in the field. The book illustrates a variety of basic algorithms for inducing simple concepts from experience, presents alternatives for organizing learned concepts into large-scale structures, and discusses adaptations of the learning methods to more complex problem-solving tasks. The chapters describe these computational techniques in detail and give examples of their operation, along with exercises and references to the literature.

This text is suitable for use in graduate courses on machine learning. Researchers and students in artificial intelligence, cognitive science, and statistics will find it a useful and informative addition to their libraries.

419 pages, Hardcover

First published September 1, 1995

Reviews

[Victor · Rating 4 out of 5]

I read this book for a course on Learning methods on artificial intelligence. As the Tom Mitchell book, Machine learning, although writteng many years ago, it is a good book for the introduction on the field. From my point of view, the book by Langley has a more theoretical approach, reviewing different paradigms of learning: logical conjuntions, threshold concepts, competitive concepts, inference network, hierarchies...