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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods John Shawe-Taylor, Nello Cristianini
Publisher: Cambridge University Press

In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how physical or genetic interaction networks Introduction. Most disease phenotypes are genetically complex, with contributions from combinations of genetic variation in different loci. Their reproducibility was evaluated by an internal cross-validation method. 4th Edition, Academic Press, 2009, ISBN 978-1-59749-272-0; Cristianini, Nello; and Shawe-Taylor, John; An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000. Support vector machines map input vectors to a higher dimensional space where a maximal separating hyperplane is constructed. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. An Introduction to Support Vector Machines and other kernel-based learning methods. [CST00]: Nello Cristianini and John Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, 1 ed., Cambridge University Press, March 2000. Bounds the influence of any single point on the decision boundary, for derivation, see Proposition 6.12 in Cristianini/Shaw-Taylor's "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods". In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression ..