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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

Support Vector Machines and Kernel Methods : The function svm() from e1071 offers an interface to the LIBSVM library and package kernlab implements a flexible framework for kernel learning (including SVMs, RVMs and other kernel learning algorithms). Scale models using state-of-the-art machine learning methods for. Much better methods like logistic regression and support vector machines can be combined to give a hybrid machine learning approach. An Introduction to Support Vector Machines and other kernel-based learning methods. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. E-Books Directory This page lists freely downloadable books. The Shogun Toolbox is an extremely impressive meta-framework for incorporating support vector machine and kernel method-based supervised machine learning into various exploratory data analysis environments. It focuses on large scale machine learning, The introduction from the main site is worth citing: (Shogun's) focus is on large scale kernel methods and especially on Support Vector Machines (SVM) [1]. Such as statistical learning theory and Support Vector Machines,. Function ctree() is based on non-parametrical conditional inference procedures for testing independence between response and each input variable whereas mob() can be used to partition parametric models. Cambridge: Cambridge University Press, 2000. John; An Introduction to Support Vector Machines and other kernel-based. CRISTIANINI, N.; SHAWE-TAYLOR, J.