Computational Learning and Probabilistic ReasoningISBN: 978-0-471-96279-3
Hardcover
338 pages
August 1996
 This is a Print-on-Demand title. It will be printed specifically to fill your order. Please allow an additional 10-15 days delivery time. The book is not returnable.
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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.
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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