Computational Learning and Probabilistic Reasoning

Computational Learning and Probabilistic Reasoning

Gammerman, A.

John Wiley & Sons Inc

05/1996

338

Dura

Inglês

9780471962793

15 a 20 dias

730

Descrição não disponível.
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 ofNatural 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 ofStatistical Decisions (A. Nagaev).

Index.
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