Adaptive computation and machine learning Bioinformatics
Contents
Series Foreword; Preface; 1 Introduction; 2 Machine-Learning Foundations: The Probabilistic Framework; 3 Probabilistic Modeling and Inference: Examples; 4 Machine Learning Algorithms; 5 Neural Networks: The Theory; 6 Neural Networks: Applications; 7 Hidden Markov Models: The Theory; 8 Hidden Markov Models: Applications; 9 Probabilistic Graphical Models in Bioinformatics; 10 Probabilistic Models of Evolution: Phylogenetic Trees; 11 Stochastic Grammars and Linguistics; 12 Microarrays and Gene Expression; 13 Internet Resources and Public Databases; A Statistics
B Information Theory, Entropy, and Relative EntropyC Probabilistic Graphical Models; D HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures; E Gaussian Processes, Kernel Methods, and Support Vector Machines; F Symbols and Abbreviations; References; Index
Summary
A guide to machine learning approaches and their application to the analysis of biological data
