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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by John Shawe-Taylor, Nello Cristianini

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods John Shawe-Taylor, Nello Cristianini ebook
Publisher: Cambridge University Press
ISBN: 0521780195, 9780521780193
Page: 189
Format: chm

An Introduction to Support Vector Machines and other kernel-based learning methods. It just struck me as an odd coincidence. "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods". We performed gene expression analysis (oligonucleotide arrays, 26,824 reporters) on 143 patients with lymph node-negative disease and tumor-free margins. In addition, to obtain good predictive power, various machine-learning algorithms such as support vector machines (SVMs), neural networks, naïve Bayes classifiers, and ensemble classifiers have been used to build classification and prediction models. As clinical parameters Methods. 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. Kernel Methods for Pattern Analysis - The Book This book is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning. Of these [35] suggested that no single-classifier method can always outperform other methods and that ensemble classifier methods outperform other classifier methods because they use various types of complementary information. Some patients with breast cancer develop local recurrence after breast-conservation surgery despite postoperative radiotherapy, whereas others remain free of local recurrence even in the absence of radiotherapy.