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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

An Introduction to Support Vector Machines and other kernel-based learning methods. According to Vladimir Vapnik in Statistical Learning Theory (1998), the assumption is inappropriate for modern large scale problems, and his invention of the Support Vector Machine (SVM) makes such assumption unnecessary. Discrimination of IBD or IBS from CTRL based upon gene-expression ratios. 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". An Introduction to Support Vector Machines and other kernel-based learning methods . Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods. Both methods are suitable for further analyses using machine learning methods such as support vector machines, logistic regression, principal components analysis or prediction analysis for microarrays. Christian Rieger, Barbara Zwicknagl; 10(Sep):2115--2132, 2009. The basic tools are sampling inequalities which apply to all machine learning problems involving penalty terms induced by kernels related to Sobolev spaces. Search for optimal SVM kernel and parameters for the regression model of cadata using rpusvm based on similar procedures explained in the text A Practical Guide to Support Vector Classification. My experience in machine learning indicates that seemingly different algorithmic/mathematical methods can be combined into a unified and coherent framework. CRISTIANINI, N.; SHAWE-TAYLOR, J. We applied three separate analytic approaches; one utilized a scoring system derived from combinations of ratios of expression levels of two genes and two different support vector machines. Shawe-Taylor & Christianini (2004). Christianini & Shawe-Taylor (2000). We introduce a new technique for the analysis of kernel-based regression problems. Kernel Methods for Pattern Analysis . Cambridge: Cambridge University Press, 2000.