Computational Learning and Probabilistic ReasoningISBN: 978-0-471-96279-3
Hardcover
338 pages
August 1996
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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 of Natural 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 of Statistical 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 of Natural 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 of Statistical Decisions (A. Nagaev).
Index.
