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

Shawe-Taylor, An introduction to sup- port vector machines and other kernel-based learning methods (Cambridge: Cambridge University Press, 2000). Summary: Multivariate kernel-based pattern classification using support vector machines (SVM) with a novel modification to obtain more balanced sensitivity and specificity on unbalanced data-sets (i.e. It just struck me as an odd coincidence. Princeton, NJ: Princeton University Press. Cristianini, N., & Shawe-Taylor, J. Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods. Many SPM users have created tools for neuroimaging analyses that are based on SPM . The distinction between Toolboxes . Christian Rieger, Barbara Zwicknagl; 10(Sep):2115--2132, 2009. Introduction to support vector machines and other kernel-based learning methods. [8] Nello Cristianini and John Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-based Learning Methods”, Cambridge University Press, 2000. The basic tools are sampling inequalities which apply to all machine learning problems involving penalty terms induced by kernels related to Sobolev spaces. We introduce a new technique for the analysis of kernel-based regression problems. "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods". Introduction The support vector machine (SVM) proposed by Vapnik [1] is a powerful methodology for solving a wide variety of problems in nonlinear classification, function estima- tion, and density estimation, which has also led to many other recent developments in kernel-based methods [2–4]. You will find here a list of these tools classified between Toolboxes, Utilities, Batch Systems and Templates. Mathematical methods in statistics.