Computational learning and probabilistic reasoning (Book, 1996) [WorldCat.org]
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Computational learning and probabilistic reasoning
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Computational learning and probabilistic reasoning

Author: A Gammerman
Publisher: Chichester ; New York : Wiley / UNICOM, ©1996.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
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. Part one describes several new inductive principles and techniques used in computational learning. Part two contains chapters on Causal Probabilistic Modes, model selection, and application of Bayesian networks to  Read more...
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Genre/Form: Kongreß
London (1995)
Congresses
Additional Physical Format: Online version:
Computational learning and probabilistic reasoning.
Chichester ; New York : Wiley, ©1996
(OCoLC)604662668
Online version:
Computational learning and probabilistic reasoning.
Chichester ; New York : Wiley, ©1996
(OCoLC)608494886
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: A Gammerman
ISBN: 0471962791 9780471962793
OCLC Number: 35701417
Description: 312 pages : illustrations ; 26 cm
Contents: Structure of statistical learning theory --
Stochastic complexity - an introduction --
MML inference of predictive trees, graphs and nets --
Learning and reasoning as information compression by multiple alignment, unification and search --
Probabilistic association and denotation in machine learning of natural language --
Causation, action, and counterfactuals --
Another semantics for Pearls action calculus --
Efficient estimation and model selection in large graphical models --
T-normal distribution on the Bayesian belief networks --
Bayesian belief network and patient treatment --
Higher order Bayesian neural network for classification and diagnosis --
Genetic algorithms applied to Bayesian networks --
Rationality, conditional independence and statistical models of competition --
Axioms for dynamic programming --
Mixture-model cluster analysis using the projection pursuit method --
Parallel Kn-nearest neighbor classifier for estimation of non-linear decision regions --
Extreme values of functionals characterizing stability of statistical decisions.
Responsibility: edited by A. Gammerman.
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Abstract:

This text is devoted to two interrelated techniques used in solving some important problems in machine intelligence and pattern recognition.  Read more...

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