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Computational Learning
and Probabilistic Reasoning
A.Gammerman (ed), J.Wiley and Sons, 1996

Computational Learning and Pobablistic Reasoning cover

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.


Part I: Generalisation Principles and Learning

  1. Structure of Statistical Learning Theory, V.Vapnik
  2. Stochastic Complexity - an Introduction, J.Rissanen
  3. MML Inference of Predictive Trees, Graphs and Nets, C.S.Wallace
  4. Learning and Reasoning as Information Compression by Multiple Alignment, Unification and Search, J.G.Wolff
  5. Probabilistic Association and Denotation in Machine Learning of Natural Language, P.Suppes and L.Liang

Part II: Causation and Model Selection

  1. Causation, Axction and Counterfactuals, J.Pearl
  2. Another Semantics for Pearl's Action Calculus, V.G.Vovk
  3. Efficient Estimation and Model Selection in Large Graphical Models, D.Wedelin
  4. T-Normal Distribution on the Bayesian Belief Networks, Yu.N.Blagoveschensky

Part III: Bayesian Belief Networks adn Hybrid Systems

  1. Bayesian Belief Networks with an Application in Specific Case Analysis, C.G.G.Aitken et al
  2. Bayesian Belief Networks and Patient Treatment, L.D.Meshalkin and E.K.Tsybulkin
  3. A Higher Order Bayesian Neural Network for Classification and Diagnosis, A.Holst and A.Lasner
  4. Genetic Algorithms Applied to Bayesian Belief Networks, P.Larrañaga et al

Part IV: Decision-Making, Optimization and Classification

  1. Rationality, Conditional Independence and Statistical Models of Competition, J.Q.Smith and C.T.J.Allard
  2. Axioms for Dynamic Programming, P.P.Shenoy
  3. Mixture-Method Cluster Analysis Usig the Projection Pursuit Method, S.Aïvazian
  4. A Parallel kn-Nearest Neighbour Classifier for Estimation of Non-linear Decision Regions, A.Kovalenko
  5. Extreme Values of Functionals Characterizing Stability of Statistical Decisions, A.V.Nagaev

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