Baldi, Pierre. <br/><br/>
Bioinformatics :  The Machine Learning Approach. 

 - 2nd ed. 
 - 1 online resource (477 pages) 
 - Adaptive Computation and Machine Learning Series .
 - Adaptive Computation and Machine Learning Series .
<br/><br/>Intro -- 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 Entropy -- C 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. 
<br/><br/> A guide to machine learning approaches and their application to the analysis of biological data. 
<br/><br/><label>ISBN: </label>9780262255707 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Bioinformatics.<br/>Molecular biology -- Computer simulation.<br/>Molecular biology -- Mathematical models.<br/>Neural networks (Computer science).<br/>Machine learning.<br/>Markov processes.
<br/><br/><label>Index Terms--Genre/Form: </label><br/>Electronic books.
<br/><br/><label>LC Class. No.: </label>QH506.B35 2001 
<br/><br/><label>Dewey Class. No.: </label>572.8/01/13