Computational Learning and Probabilistic Reasoning / Edition 1by A. Gammerman, Gammerman
Pub. Date: 07/16/1996
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
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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Table of Contents
Partial table of contents:
GENERALISATION PRINCIPLES AND LEARNING.
Structure of Statistical Learning Theory (V. Vapnik).
MML Inference of Predictive Trees, Graphs and Nets (C. Wallace).
Probabilistic Association and Denotation in Machine Learning of Natural Language (P. Suppes & L. Liang).
CAUSATION AND MODEL SELECTION.
Causation, Action, and Counterfactuals (J. Pearl).
Efficient Estimation and Model Selection in Large Graphical Models (D. Wedelin).
BAYESIAN BELIEF NETWORKS AND HYBRID SYSTEMS.
Bayesian Belief Networks and Patient Treatment (L. Meshalkin & E. Tsybulkin).
DECISION-MAKING, OPTIMIZATION AND CLASSIFICATION.
Axioms for Dynamic Programming (P. Shenoy).
Extreme Values of Functionals Characterizing Stability of Statistical Decisions (A. Nagaev).
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