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The Resource Bioinformatics : the machine learning approach, Pierre Baldi, Søren Brunak, (electronic resource)

Bioinformatics : the machine learning approach, Pierre Baldi, Søren Brunak, (electronic resource)

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
Member of
Cataloging source
N$T
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
  • standards specifications
  • bibliography
Series statement
Adaptive computation and machine learning
Bioinformatics : the machine learning approach, Pierre Baldi, Søren Brunak, (electronic resource)
Label
Bioinformatics : the machine learning approach, Pierre Baldi, Søren Brunak, (electronic resource)
Link
http://libproxy.rpi.edu/login?url=http://www.netlibrary.com/urlapi.asp?action=summary&v=1&bookid=1441
Publication
Note
"A Bradford book."
Related Contributor
Related Location
Related Agents
Related Authorities
Related Subjects
Related Items
Bibliography note
Includes bibliographical references (p. 319-346) and index
Color
multicolored
Contents
  • Bayesian Inference and Induction
  • Model Structures: Graphical Models and Other Tricks
  • Probabilistic Modeling and Inference: Examples
  • The Simplest Sequence Models
  • Statistical Mechanics
  • Machine Learning Algorithms
  • Dynamic Programming
  • Gradient Descent
  • EM/GEM Algorithms
  • Markov Chain Monte Carlo Methods
  • Biological Data in Digital Symbol Sequences
  • Simulated Annealing
  • Evolutionary and Genetic Algorithms
  • Learning Algorithms: Miscellaneous Aspects
  • Neural Networks: The Theory
  • Universal Approximation Properties
  • Priors and Likelihoods
  • Learning Algorithms: Backpropagation
  • Neural Networks: Applications
  • Sequence Encoding and Output Interpretation
  • Prediction of Protein Secondary Structure
  • Genomes--Diversity, Size, and Structure
  • Prediction of Signal Peptides and Their Cleavage Sites
  • Applications for DNA and RNA Nucleotide Sequences
  • Hidden Markov Models: The Theory
  • Prior Information and Initialization
  • Likelihood and Basic Algorithms
  • Learning Algorithms
  • Applications of HMMs: General Aspects
  • Hidden Markov Models: Applications
  • Protein Applications
  • DNA and RNA Applications
  • Proteins and Proteomes
  • Conclusion: Advantages and Limitations of HMMs
  • Hybrid Systems: Hidden Markov Models and Neural Networks
  • Introduction to Hybrid Models
  • On the Information Content of Biological Sequences
  • Prediction of Molecular Function and Structure
  • Machine Learning Foundations: The Probabilistic Framework
  • Introduction: Bayesian Modeling
  • The Cox-Jaynes Axioms
http://library.link/vocab/cover_art
https://contentcafe2.btol.com/ContentCafe/Jacket.aspx?Return=1&Type=S&Value=9780585038025&userID=ebsco-test&password=ebsco-test
Dimensions
unknown
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b3127256'}
Extent
1 online resource (xviii, 351 p.)
Form of item
online
Isbn
9780585038025
Isbn Type
(electronic bk.)
Other physical details
ill. (some col.)
Specific material designation
remote

Library Locations

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      42.729766 -73.682577
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