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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

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. Almost all of these machine learning processes are based on support vector machines or related algorithms, which at first glance seem unapproachably complex. 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 : PDF eBook Download. The subsequent predictive models are trained with support vector machines introducing the variables sequentially from a ranked list based on the variable importance. Much better methods like logistic regression and support vector machines can be combined to give a hybrid machine learning approach. Support vector machines are a relatively new classification or prediction method developed by Cortes and Vapnik21 in the 1990s as a result of the collaboration between the statistical and the machine-learning research communities. 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. 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. 4th Edition, Academic Press, 2009, ISBN 978-1-59749-272-0; Cristianini, Nello; and Shawe-Taylor, John; An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000. October 24th, 2012 reviewer Leave a comment Go to comments. Machine-learning approaches, which include neural networks, hidden Markov models, belief networks, support vector and other kernel-based machines, are ideally suited for domains characterized by the existence of large amounts of data, . [8] Nello Cristianini and John Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-based Learning Methods”, Cambridge University Press, 2000. An Introduction to Support Vector Machines and other kernel-based learning methods. In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression .. Publicus Groupe SA, issued in February 2012, giving a judicial imprimatur to use of “predictive coding” and other sophisticated iterative sampling techniques in satisfaction of discovery obligations, should assist in paving the way toward greater acceptance of these new methods. Science Ebook Collections 0057 An Introduction to Support Vector Machines and Other Kernel-based Learning Methods Cristianini N.