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The Resource Bioinformatics : the machine learning approach, Pierre Baldi, Søren Brunak

Bioinformatics : the machine learning approach, Pierre Baldi, Søren Brunak

Label
Bioinformatics : the machine learning approach
Title
Bioinformatics
Title remainder
the machine learning approach
Statement of responsibility
Pierre Baldi, Søren Brunak
Creator
Contributor
Subject
Language
eng
Summary
Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed at two types of researchers and students. First are the biologists and biochemists who need to understand new data-driven algorithms, such as neural networks and hidden Markov models, in the context of biological sequences and their molecular structure and function. Second are those with a primary background in physics, mathematics, statistics, or computer science who need to know more about specific applications in molecular biology
Cataloging source
DLC
http://library.link/vocab/creatorName
Baldi, Pierre
Illustrations
illustrations
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
Brunak, Søren
Series statement
Adaptive computation and machine learning
http://library.link/vocab/subjectName
  • Molecular biology
  • Molecular biology
  • Neural networks (Computer science)
  • Machine learning
  • Markov processes
Label
Bioinformatics : the machine learning approach, Pierre Baldi, Søren Brunak
Instantiates
Publication
Bibliography note
Includes bibliographical references (p. 319-346) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 4.
  • Machine Learning Algorithms
  • 5.
  • Neural Networks: The Theory
  • 6.
  • Neural Networks: Applications
  • 7.
  • Hidden Markov Models: The Theory
  • 8.
  • Hidden Markov Models: Applications
  • Series Forward
  • 9.
  • Hybrid Systems: Hidden Markov Models and Neural Networks
  • 10.
  • Probabilistic Models of Evolution: Phylogenetic Trees
  • 11.
  • Stochastic Grammars and Linguistics
  • 12.
  • Internet Resources and Public Databases
  • Appendix A Statistics
  • Appendix B Information Theory, Entropy, and Relative Entropy
  • Preface
  • Appendix C Probabilistic Graphical Models
  • Appendix D HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures
  • Appendix E List of Main Symbols and Abbreviations
  • References
  • Index
  • 1.
  • Introduction
  • 2.
  • Machine Learning Foundations: The Probabilistic Framework
  • 3.
  • Probabilistic Modeling and Inference: Examples
Dimensions
24 cm.
Extent
xviii, 351 p
Isbn
9780262024426
Lccn
97036102
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
ill. (some col.)
Label
Bioinformatics : the machine learning approach, Pierre Baldi, Søren Brunak
Publication
Bibliography note
Includes bibliographical references (p. 319-346) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • 4.
  • Machine Learning Algorithms
  • 5.
  • Neural Networks: The Theory
  • 6.
  • Neural Networks: Applications
  • 7.
  • Hidden Markov Models: The Theory
  • 8.
  • Hidden Markov Models: Applications
  • Series Forward
  • 9.
  • Hybrid Systems: Hidden Markov Models and Neural Networks
  • 10.
  • Probabilistic Models of Evolution: Phylogenetic Trees
  • 11.
  • Stochastic Grammars and Linguistics
  • 12.
  • Internet Resources and Public Databases
  • Appendix A Statistics
  • Appendix B Information Theory, Entropy, and Relative Entropy
  • Preface
  • Appendix C Probabilistic Graphical Models
  • Appendix D HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures
  • Appendix E List of Main Symbols and Abbreviations
  • References
  • Index
  • 1.
  • Introduction
  • 2.
  • Machine Learning Foundations: The Probabilistic Framework
  • 3.
  • Probabilistic Modeling and Inference: Examples
Dimensions
24 cm.
Extent
xviii, 351 p
Isbn
9780262024426
Lccn
97036102
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
ill. (some col.)

Library Locations

    • Brunswick Library StoreBorrow it
      Liverpool, GB
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