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An Introduction to Support Vector Machines and
An Introduction to Support Vector Machines and

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



Download An Introduction to Support Vector Machines and Other Kernel-based Learning Methods




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


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 .. 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. An Introduction to Support Vector Machines and other kernel-based learning methods. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Hardcover) by Nello Cristianini, John Shawe-Taylor. It too is suited for an introduction to Support Vector Machines. Publisher: Cambridge University Press (2000). Cambridge: Cambridge University Press, 2000. CRISTIANINI, N.; SHAWE-TAYLOR, J. Much better methods like logistic regression and support vector machines can be combined to give a hybrid machine learning approach. The book is titled Support Vector Machines and other Kernel Based Learning methods and is authored by Nello Cristianini and John-Shawe Taylor. The subsequent predictive models are trained with support vector machines introducing the variables sequentially from a ranked list based on the variable importance. These approaches are then compared to traditional wrapper-based feature selection implementations based on support vector machines (SVM) to reveal the relative speed-up and to assess the feasibility of the new algorithm.

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