Computational Learning and Probabilistic Reasoning
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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.
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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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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
