Bioinformatics: The Machine Learning Approach Book

Name: Bioinformatics: The Machine Learning Approach, An unprecedented w
Brand: MIT Press
SKU: 9780262025065
Price: 99.99 USD
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Bioinformatics: The Machine Learning Approach, An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidl, Bioinformatics: The Machine Learning Approach

4 out of 5 stars based on 2 reviews

Bioinformatics: The Machine Learning Approach
Written by author Pierre Baldi
Published by MIT Press, August 2001
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidl
A guide to machine learning approaches and their application to the analysis of biological data. ( Nature Biotechnology, March 1999) - Terry Gaasterland With this work, Baldi and Brunak have provided a sound foundation for the proce

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Book Categories

Authors

Series Foreword
Preface
1 Introduction 1
2 Machine Learning Foundations: The Probabilistic Framework 39
3 Probabilistic Modeling and Inference: Examples 59
4 Machine Learning Algorithms 73
5 Neural Networks: The Theory 91
6 Neural Networks: Applications 105
7 Hidden Markov Models: The Theory 143
8 Hidden Markov Models: Applications 167
9 Hybrid Systems: Hidden Markov Models and Neural Networks 201
10 Probabilistic Models of Evolution: Phylogenetic Trees 217
11 Stochastic Grammars and Linguistics 229
12 Internet Resources and Public Databases 251
A: Statistics 271
B Information Theory, Entropy, and Relative Entropy 281
C Probabilistic Graphical Models 289
D HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures 299
E: List of Main Symbols and Abbreviations 311
References 319
Index 347

Title: Bioinformatics: The Machine Learning Approach

Manufacturer:

MIT Press

Item Number: 9780262025065

Publication Date: August 2001

Product Description: Full Name: Bioinformatics: The Machine Learning Approach; Short Name:Bioinformatics

Universal Product Code (UPC): 9780262025065

WonderClub Stock Keeping Unit (WSKU): 9780262025065

Rating: 4/5 based on 2 Reviews

Image Location: https://wonderclub.com/images/covers/50/65/9780262025065.jpg

Category: Media >> Books

Weight: 0.200 kg (0.44 lbs)

Width: 7.000 cm (2.76 inches)

Heigh : 9.000 cm (3.54 inches)

Depth: 1.250 cm (0.49 inches)

Date Added: August 25, 2020, Added By: Ross

Date Last Edited: August 25, 2020, Edited By: Ross

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Garett Horgen

reviewed Bioinformatics: The Machine Learning Approach on March 31, 2009

Bioinformatics: The Machine Learning Approach

Read chapter 5 (neural networks) and skimmed 6 (applications of neural networks); good theoretical foundation.

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Bioinformatics: The Machine Learning Approach, An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidl, Bioinformatics: The Machine Learning Approach

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Bioinformatics: The Machine Learning Approach Book

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	Series Foreword
	Preface
1	Introduction	1
2	Machine Learning Foundations: The Probabilistic Framework	39
3	Probabilistic Modeling and Inference: Examples	59
4	Machine Learning Algorithms	73
5	Neural Networks: The Theory	91
6	Neural Networks: Applications	105
7	Hidden Markov Models: The Theory	143
8	Hidden Markov Models: Applications	167
9	Hybrid Systems: Hidden Markov Models and Neural Networks	201
10	Probabilistic Models of Evolution: Phylogenetic Trees	217
11	Stochastic Grammars and Linguistics	229
12	Internet Resources and Public Databases	251
	A: Statistics	271
B	Information Theory, Entropy, and Relative Entropy	281
C	Probabilistic Graphical Models	289
D	HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures	299
	E: List of Main Symbols and Abbreviations	311
	References	319
	Index	347