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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 the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. An Introduction to Support Vector Machines and other kernel-based learning methods. The book is titled Support Vector Machines and other Kernel Based Learning methods and is authored by Nello Cristianini and John-Shawe Taylor. Collective Intelligence" first, then "Collective Intelligence in Action". In this work In addition, it has been shown that SNP markers in these candidate genes could predict whether a person has CFS using an enumerative search method and the support vector machine (SVM) algorithm [9]. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. My experience in machine learning indicates that seemingly different algorithmic/mathematical methods can be combined into a unified and coherent framework. Cambridge: Cambridge University Press, 2000. The first one shows how easy it is to implement basic algorithms, the second one would show you how to use existing open source projects related to machine learning. It too is suited for an introduction to Support Vector Machines. CRISTIANINI, N.; SHAWE-TAYLOR, J. Witten IH, Frank E: Data Mining: Practical Machine Learning Tools and Techniques. Support Vector Machine (SVM) is a supervised learning algorithm developed by Vladimir Vapnik and his co-workers at AT&T Bell Labs in the mid 90's.