ISBN 0-262-02506-X

Author(s): P.Baldi and S. Brunak

Cloth, 400 pages; August 2001; MIT Press

TOC:

1. Introduction
2. Machine-Learning Foundations: The Probabilistic Framework
3. Probabilistic Modeling & 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
11. Stochastic Grammars & Linguistics
12. Microarrays & Gene Expression
13. Internet Resources & Public Databases

    A. Statistics
    B. Information Theory, Entropy, & Relative
    C. Probabilistic Graphical Models
    D. HMM Technicalities, Scaling, Periodic
    E. Gaussian Processes, Kernel Methods, and Support Vector Machines
    F. Symbols and Abbreviations
    References Index