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Computational Molecular Biology Ser.: Kernel Methods in Computational Biology by Sorin Istrail, Bernhard Schölkopf and Pavel A. Pevzner (2004, Hardcover)
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- Sofort-KaufenKernel Methods in Computational Biology ~ Scholkopf, Tsuda & Vert ~ Hardcover
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Über dieses Produkt
Product Identifiers
PublisherMIT Press
ISBN-100262195097
ISBN-139780262195096
eBay Product ID (ePID)30217712
Product Key Features
Number of Pages410 Pages
LanguageEnglish
Publication NameKernel Methods in Computational Biology
Publication Year2004
SubjectLife Sciences / Microbiology, General, Life Sciences / Biology
TypeTextbook
AuthorSorin Istrail, Bernhard Schölkopf, Pavel A. Pevzner
Subject AreaComputers, Science
SeriesComputational Molecular Biology Ser.
FormatHardcover
Dimensions
Item Height1 in
Item Weight37.1 Oz
Item Length10.2 in
Item Width8 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2003-068640
Dewey Edition22
Reviews"This timely collection will be an asset to anyone working with microarray data, and those involved with computational biology more generally should be aware of it." -Jun Liu, Professor of Statistics, Harvard University
Grade FromCollege Graduate Student
IllustratedYes
Dewey Decimal570/.285
SynopsisA detailed overview of current research in kernel methods and their application to computational biology., A detailed overview of current research in kernel methods and their application to computational biology. Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology. Following three introductory chapters--an introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biology--the book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods.
LC Classification NumberQH324.2.K47 2004
