There is an abundance of literature on Neural Networks (NNs). However, it is hard to find a text on NNs that provides a happy balance among good pedagogy, theory, and practice. For example, most introductory NN courses are taught with a collection of loosely related papers, excerpts from books, or software packages. A novice attempting to learn the subject without a good instructor inevitably becomes lost in an array of inconsistent terminology, complicated mathematics, and diverse methodologies and usually does not obtain a firm grasp of the basic foundations of the subject. Steve Gallant's book "Neural Network Learning and Expert Systems" is a good effort towards alleviating these problems. In addition, the book introduces a method to integrate the best features of NNs and expert systems (ESs). The goals of the book are (1) to provide a systematic development of NN learning algorithms suitable for researchers and students and (2) to present NN expert systems. The former goal is largely met and thus renders the book well-suited for self-directed study or for primary reading in a graduate or upper-level undergraduate NN course. The latter goal is met also, although the book lacks sufficient comparison with alternative approaches to the integration of NNs and ESs.
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Book review: Neural Network Learning and Expert Systems by Stephen L. Gallant (MIT Press, Cambridge, MA, 1993)
Reviewer-Minior, Dorothy; Reviewer-Lewis, Lundy
Follow ACM SIGART Bulletin
, Volume 5 (1)
Association for Computing Machinery – Jan 1, 1994
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