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Bioinformatics : The Machine Learning Approach.

By: Contributor(s): Material type: TextTextSeries: Adaptive Computation and Machine Learning SeriesPublisher: Cambridge : MIT Press, 2001Copyright date: ©2001Edition: 2nd edDescription: 1 online resource (477 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780262255707
Subject(s): Genre/Form: Additional physical formats: Print version:: BioinformaticsDDC classification:
  • 572.8/01/13
LOC classification:
  • QH506.B35 2001
Online resources:
Contents:
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.
Summary: A guide to machine learning approaches and their application to the analysis of biological data.
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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.

A guide to machine learning approaches and their application to the analysis of biological data.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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