Computational Learning and Probabilistic Reasoning

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A. Gammerman
Wiley, Aug 6, 1996 - Computers - 338 pages
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Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for handling complex problems in medicine, commerce and finance. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning. Part II describes Causation and Model Selection including the graphical probabilistic models that exploit the independence relationships presented in the graphs, and applications of Bayesian networks to multivariate statistical analysis. Part III includes case studies and descriptions of Bayesian Belief Networks and Hybrid Systems. Finally, Part IV on Decision-Making, Optimization and Classification describes some related theoretical work in the field of probabilistic reasoning. Statisticians, IT strategy planners, professionals and researchers with interests in learning, intelligent databases and pattern recognition and data processing for expert systems will find this book to be an invaluable resource. Real-life problems are used to demonstrate the practical and effective implementation of the relevant algorithms and techniques.

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Contents

Structure of Statistical Learning Theory
3
database
28
Stochastic Complexity an Introduction
33
Learning and Reasoning as Information Compression
67
Probabilistic Association and Denotation in Machine
87
Causation Action and Counterfactuals
103
Another Semantics for Pearls Action Calculus
125
Efficient Estimation and Model Selection in Large Graphical
145
Baysian Belief Networks and Patient Treatment
185
A Higher Order Bayesian Neural Network for Classification
199
Genetic Algorithms Applied to Bayesian Networks
211
DecisionMaking Optimization
235
Axioms for Dynamic Programming
259
MixtureModel Cluster Analysis Using the Projection
277
A Parallel fcnNearest Neighbour Classifier for Estimation
287
Extreme Values of Functionals Characterizing Stability
295

TNormal Distribution on the Bayesian Belief Networks
161
Bayesian Belief Networks with an Application in Specific
169
Index
309
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