This book is devoted to two interrelated techniques in solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. It is divided into four parts, the first of which describes several new inductive principles and techniques used in computational learning. The second part contains papers on Bayesian and Causal Belief networks. Part three includes chapters on case studies and descriptions of several hybrid systems and the final part describes some ...
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This book is devoted to two interrelated techniques in solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. It is divided into four parts, the first of which describes several new inductive principles and techniques used in computational learning. The second part contains papers on Bayesian and Causal Belief networks. Part three includes chapters on case studies and descriptions of several hybrid systems and the final part describes some related theoretical work in the field of probabilistic reasoning.
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