About the Book

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.

Product Details

Media: Hardcover Book, 419 pages

Language: English

Publisher: Morgan Kaufmann Publishers

Edition: Unknown

ISBN-10: 1558603018

Publication Date: Unknown

ISBN-13: 9781558603011

Dimensions: 18.67 x 23.75 x 2.79 cm

Shipping Weight: 0.89 kg

• Categories: General Computer Reference