Introduction to Machine Learning and Bioinformatics

Author:   Sushmita Mitra ,  Sujay Datta ,  Theodore Perkins ,  George Michailidis
Publisher:   Taylor & Francis Inc
ISBN:  

9781584886822


Pages:   384
Publication Date:   05 June 2008
Format:   Hardback
Availability:   In Print   Availability explained
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Introduction to Machine Learning and Bioinformatics


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Author:   Sushmita Mitra ,  Sujay Datta ,  Theodore Perkins ,  George Michailidis
Publisher:   Taylor & Francis Inc
Imprint:   Chapman & Hall/CRC
Weight:   1.080kg
ISBN:  

9781584886822


ISBN 10:   158488682
Pages:   384
Publication Date:   05 June 2008
Audience:   College/higher education ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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! The stated audience for this book is M.S. and Ph.D. students in bioinformatics, machine intelligence, applied statistics, biostatistics, computer science, and related areas. ! a well-written collection from multiple authors that I recommend for the intended audience. Several chapters include exercises. --Technometrics, November 2009, Vol. 51, No. 4 !a good text/reference book that summarizes the latest developments in the interface between bioinformatics and machine learning and offer[s] 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. It is also very readable for a variety of interested learners, researchers, and audiences from various backgrounds and disciplines. ! --Biometrics, March 2009 ! a well-structured book that is a good starting point for machine learning in bioinformatics. ! Using many popular examples, the statistical theory becomes comprehensible and bioinformatics examples motivate [readers] to apply the concepts to real data. --Markus Schmidberger, Journal of Statistical Software, November 2008


... The stated audience for this book is M.S. and Ph.D. students in bioinformatics, machine intelligence, applied statistics, biostatistics, computer science, and related areas. ... a well-written collection from multiple authors that I recommend for the intended audience. Several chapters include exercises. -Technometrics, November 2009, Vol. 51, No. 4 ...a good text/reference book that summarizes the latest developments in the interface between bioinformatics and machine learning and offer[s] 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. It is also very readable for a variety of interested learners, researchers, and audiences from various backgrounds and disciplines. ... -Biometrics, March 2009 ... a well-structured book that is a good starting point for machine learning in bioinformatics. ... Using many popular examples, the statistical theory becomes comprehensible and bioinformatics examples motivate [readers] to apply the concepts to real data. -Markus Schmidberger, Journal of Statistical Software, November 2008


Author Information

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

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