Kernel Methods in Computational Biology (Computational Molecular Biology)

ISBN 10: 0262195097 / ISBN 13: 9780262195096

Edité par MIT Press, 2004

A propos de cet article

Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Acceptable condition! Any other included accessories are also in Acceptable condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear such as cover tears discoloration, staining, marks, scuffs, etc. All pages intact. N° de réf. du vendeur GWSVV.0262195097.A

Détails bibliographiques

Titre : Kernel Methods in Computational Biology (...

Éditeur : MIT Press

Date d'édition : 2004

Reliure : Couverture rigide

Etat : acceptable

A propos de ce titre

Synopsis :

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.

À propos de l?auteur:

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.

Koji Tsuda is a Research Scientist at the Max Planck Institute and a Researcher at AIST Computational Biology Research Center, Tokyo.

Jean-Philippe Vert is Researcher and Leader of the Bioinformatics Group at École des Mines de Paris.