Bioinformatics : the machine learning approach /
Main Author: | |
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Corporate Author: | |
Other Authors: | , |
Format: | Book |
Language: | English |
Published: |
Cambridge, Mass. :
MIT Press,
c2001
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Edition: | 2nd ed |
Series: | Adaptive computation and machine learning
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Subjects: |
Table of Contents:
- Introduction
- Machine-learning foundations : the probabilistic framework
- Probabilistic modeling and inference : examples
- Machine learning algorithms
- Neural networks : the theory
- Neural networks : applications
- Hidden Markov models: the theory
- Hidden Markov models: applications
- Probabilistic graphical models in bioinformatics
- Probabilistic models of evolution : phylogenetic trees
- Stochastic grammars and linguistics
- Microarrays and gene expression
- Internet resources and public databases
- Statistics
- Information theory, entropy, and relative entropy
- Probabilistic graphical models
- HMM technicalities, scaling, periodic architectures, state functions, and dirichlet mixtures
- Gaussian processes, kernel methods and support vector machines
- Symbols and abbreviations