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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 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. [CST00]: Nello Cristianini and John Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, 1 ed., Cambridge University Press, March 2000. More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. The results show that In [6], a new supervised machine learning method was proposed to handle such problem based on conditional random fields (CRFs), and the results had shown a promising future. We follow the method introduced in [21] to solve this problem. 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. Download free An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini , John Shawe-Taylor B01_0506 John Shawe-Taylor Nello Cristianini pdf chm epub format. Based upon the framework of the structural support vector machines, this paper proposes two approaches to the depth restoration towards different scenes, that is, margin rescaling and the slack rescaling. E-Books Directory This page lists freely downloadable books.