Includes bibliographical references (pages 511-521) and index.
CONTENTS NOTE
Text of Note
Underlying theory -- Approximate and feasible learning -- Approximate design -- Problem formulation -- Solution and principles of its approximation: learning part -- Solution and principles of its approximation: design part -- Learning with normal factors and components -- Design with normal mixtures -- Learning with Markov-chain factors and components -- Design with Markov-chain mixtures -- Sandwich BMTB for mixture initiation -- Mixed mixtures -- Applications of the advisory system -- Concluding remarks.
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SUMMARY OR ABSTRACT
Text of Note
Accompanying CD-ROM ... "contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the most important and complex areas of the material presented."--Page 4 of cover.