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eBook Introduction to Machine Learning and Bioinformatics (Chapman  Hall/CRC Computer Science  Data Analysis) ePub

eBook Introduction to Machine Learning and Bioinformatics (Chapman Hall/CRC Computer Science Data Analysis) ePub

by Sushmita Mitra,Theodore Perkins,George Michailidis,Sujay Datta

  • ISBN: 158488682X
  • Category: Computer Science
  • Subcategory: Computers
  • Author: Sushmita Mitra,Theodore Perkins,George Michailidis,Sujay Datta
  • Language: English
  • Publisher: Chapman and Hall/CRC; 1 edition (June 5, 2008)
  • Pages: 384
  • ePub book: 1834 kb
  • Fb2 book: 1444 kb
  • Other: mobi lrf rtf lit
  • Rating: 4.2
  • Votes: 534

Description

Introduction to Machine . .has been added to your Cart. a good text/reference book that summarizes the latest developments in the interface between bioinformatics and machine learning and offer a thorough introduction to each field

Introduction to Machine . a good text/reference book that summarizes the latest developments in the interface between bioinformatics and machine learning and offer a thorough introduction to each field. One of the strengths of this book is the clear notation with a mathematical and statistical flavor, which will be attractive to Biometrics readers, especially to those new to statistical learning and data mining. ―Biometrics, March 2009.

Start by marking Introduction to Machine Learning and Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) as Want to Read

Start by marking Introduction to Machine Learning and Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis) as Want to Read: Want to Read savin. ant to Read.

Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the .

Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. Examines Connections between Machine Learning & Bioinformatics.

Chapman & Hall/ CRC Computer Science & Data Analysis. Other books in this series. Introduction to Machine Learning and Bioinformatics. By (author) Sushmita Mitra, By (author) Sujay Datta, By (author) Theodore Perkins, By (author) George Michailidis. Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today's biological experiments. Format Hardback 384 pages.

by: Sushmita Mitra; Sujay Datta; Theodore Perkins; George Michailidis. Publisher: Chapman & Hall. Print ISBN: 9780367387235, 0367387239. eText ISBN: 9781439890851, 1439890854. Examines Connections between Machine Learning & Bio. Additional ISBNs. 9781584886822, 158488682X.

9. Control Questions. 1. Key parts of predictive models.

biological data analysis within R, or how to solve statistical. problems whit R, but how to implement R-code for the biological. Detailed material can then always be found in.

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eBook Rental - By Sushmita Mitra; Sujay Datta; Theodore Perkins .

Bioinformatics Challenges at the Interface of Biology and Computer Science: Mind the Gap (eBook). Chapman and Hall/CRC Computer Science and Data Analysis: R Programming for Bioinformatics by Robert Gentleman Hardcover, Hardcover).

Автор: Mitra Название: Introduction to Machine Learning and Bioinformatics Издательство . A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis.

Lucidly Integrates Current Activities

Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.

Examines Connections between Machine Learning & Bioinformatics

The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website.

Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems

Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.