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Author Baldi, Pierre.
Title Bioinformatics : the machine learning approach / Pierre Baldi, Søren Brunak.
Imprint Cambridge, Mass. : MIT Press, [2001]
Edition Second edition.

Author Baldi, Pierre.
Series Adaptive computation and machine learning
Adaptive computation and machine learning.
Subject Bioinformatics.
Molecular biology -- Computer simulation.
Molecular biology -- Mathematical models.
Neural networks (Computer science)
Machine learning.
Markov processes.
Computational Biology -- methods.
Artificial Intelligence.
Markov Chains.
Models, Theoretical.
Neural Networks, Computer.
Alt Name Brunak, Søren.
Description xxi, 452 pages : illustrations ; 24 cm.
Edition Second edition.
Note "A Bradford book"
Bibliography Note Includes bibliographical references (p. 409-445).
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 : phylogenitc trees -- Stochastic grammars and linguistics -- Microarrays and gene expression -- Internet resources and public databases --
ISBN 026202506X (hc. : alk. paper)
OCLC # 45951728
Table of Contents
 Series Foreword 
2Machine-Learning Foundations: The Probabilistic Framework47
3Probabilistic Modeling and Inference: Examples67
4Machine Learning Algorithms81
5Neural Networks: The Theory99
6Neural Networks: Applications113
7Hidden Markov Models: The Theory165
8Hidden Markov Models: Applications189
9Probabilistic Graphical Models in Bioinformatics225
10Probabilistic Models of Evolution: Phylogenetic Trees265
11Stochastic Grammars and Linguistics277
12Microarrays and Gene Expression299
13Internet Resources and Public Databases323
 A: Statistics347
 B: Information Theory, Entropy, and Relative Entropy357
 C: Probabilistic Graphical Models365
DHMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures375
EGaussian Processes, Kernel Methods, and Support Vector Machines387
 F: Symbols and Abbreviations399

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