Computational learning and probabilistic reasoning
This book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. Part one describes several new inductive principles and techniques used in computational learning. Part two contains chapters on Causal Probabilistic Modes, model selection, and application of Bayesian networks to multivariate statistical analysis -- The third part is on Bayesian Belief Networks and hybrid systems. The fourth part describes some related theoretical work in the field of probabilistic reasoning
Kongreß London 1995
312 pages : illustrations ; 26 cm
9780471962793, 0471962791
35701417
Structure of statistical learning theory
Stochastic complexity - an introduction
MML inference of predictive trees, graphs and nets
Learning and reasoning as information compression by multiple alignment, unification and search
Probabilistic association and denotation in machine learning of natural language
Causation, action, and counterfactuals
Another semantics for Pearls action calculus
Efficient estimation and model selection in large graphical models
T-normal distribution on the Bayesian belief networks
Bayesian belief network and patient treatment
Higher order Bayesian neural network for classification and diagnosis
Genetic algorithms applied to Bayesian networks
Rationality, conditional independence and statistical models of competition
Axioms for dynamic programming
Mixture-model cluster analysis using the projection pursuit method
Parallel Kn-nearest neighbor classifier for estimation of non-linear decision regions
Extreme values of functionals characterizing stability of statistical decisions