Computational Learning
and Probabilistic Reasoning
A.Gammerman (ed), J.Wiley and Sons, 1996
This book provides a unified coverage of the latest resarch and
applications in two interrelated problem-solving techniques in machine
intelligence and pattern recognition: probabilistic reasoning and
computational learning.
Part I covers Generalisation Principles and Learning and
describes several new inductive techniques used in computational
learning.
Part II describes Causation and Model Selection including
graphical probabilistic models and applications of Bayesian networks
to multivariate statistical analysis.
Part III includes case studies and describes Bayesian Belief
Networks and Hybrid Systems.
Part IV covers Descision-Making, Optimisation and Classification and describes some related theoretical work in the field of probabilistic
reasoning.
The book includes contributions from such luminaries as Vapnik, Rissanen, Wallace, Pearl and Vovk.
Contents
Part I: Generalisation Principles and Learning
- Structure of Statistical Learning Theory, V.Vapnik
- Stochastic Complexity - an Introduction, J.Rissanen
- MML Inference of Predictive Trees, Graphs and Nets, C.S.Wallace
- Learning and Reasoning as Information Compression by Multiple
Alignment, Unification and Search, J.G.Wolff
- Probabilistic Association and Denotation in Machine Learning
of Natural Language, P.Suppes and L.Liang
Part II: Causation and Model Selection
- Causation, Axction and Counterfactuals, J.Pearl
- Another Semantics for Pearl's Action Calculus,
V.G.Vovk
- Efficient Estimation and Model Selection in Large
Graphical Models, D.Wedelin
- T-Normal Distribution on the Bayesian Belief
Networks, Yu.N.Blagoveschensky
Part III: Bayesian Belief Networks adn Hybrid Systems
- Bayesian Belief Networks with an Application
in Specific Case Analysis, C.G.G.Aitken et al
- Bayesian Belief Networks and Patient Treatment,
L.D.Meshalkin and E.K.Tsybulkin
- A Higher Order Bayesian Neural Network for Classification
and Diagnosis, A.Holst and A.Lasner
- Genetic Algorithms Applied to Bayesian Belief
Networks, P.Larrañaga et al
Part IV: Decision-Making, Optimization and Classification
- Rationality, Conditional Independence and Statistical
Models of Competition, J.Q.Smith and C.T.J.Allard
- Axioms for Dynamic Programming, P.P.Shenoy
- Mixture-Method Cluster Analysis Usig the Projection
Pursuit Method, S.Aïvazian
- A Parallel kn-Nearest Neighbour Classifier
for Estimation of Non-linear Decision Regions,
A.Kovalenko
- Extreme Values of Functionals Characterizing
Stability of Statistical Decisions, A.V.Nagaev
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