Machine Learning: An Artificial Intelligence Approach - Comprehensive Guide for AI Developers & Data Scientists | Applications in Predictive Analytics, NLP & Computer Vision
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Product Description
The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn ing processes is of great significance to fields concerned with understanding in telligence. Such fields include cognitive science, artificial intelligence, infor mation science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines. The recent observance of the silver anniversary of artificial intelligence has been heralded by a surge of interest in machine learning-both in building models of human learning and in understanding how machines might be endowed with the ability to learn. This renewed interest has spawned many new research projects and resulted in an increase in related scientific activities. In the summer of 1980, the First Machine Learning Workshop was held at Carnegie-Mellon University in Pittsburgh. In the same year, three consecutive issues of the Inter national Journal of Policy Analysis and Information Systems were specially devoted to machine learning (No. 2, 3 and 4, 1980). In the spring of 1981, a special issue of the SIGART Newsletter No. 76 reviewed current research projects in the field. . This book contains tutorial overviews and research papers representative of contemporary trends in the area of machine learning as viewed from an artificial intelligence perspective. As the first available text on this subject, it is intended to fulfill several needs.
Customer Reviews
I purchased the paperback version of this book, which has the title Machine Learning: An Artificial Intelligence Approach (Symbolic Computation).Amazon lists this as the paperback version of the Machine Learning text by Mitchell.However, the differences between the hardcover and the paperback are huge. The paperback was published in 1983. The Mitchell hardcover book was published fourteen years later in 1997. The authors on the paperback are labeled as "Edited by Ryszard S. Michalski, Jaime G. Carbonell, and Tom M. Mitchell," while the hardcover lists the author as Tom Mitchell.Critically, there are also significant differences in the contents. For example, chapter 8 in the hardcover version is titled Instance-based Learning. Chapter 8 in the paperback is titled Using Proofs and Refutations to Learn from Experience. Chapter 3 of the hardcover is called Decision Tree Learning. In the paperback, chapter 3 is called A Comparative Review of Selected Methods for Learning from Examples. This is not an issue of reordering; the paperback has totally different chapters. Use the Look Inside tool to see for yourself.I suspect from the Symbolic Computation logo on the top of the cover, as well as from the lack of a true "author," that the paperback edition is a collection of papers from the Symbolic Computation journal, and not a true textbook. The similar titles and Mitchell's name on both perhaps created confusion at Amazon.
