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

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


An.Introduction.to.Support.Vector.Machines.and.Other.Kernel.based.Learning.Methods.pdf
ISBN: 0521780195,9780521780193 | 189 pages | 5 Mb


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
Publisher: Cambridge University Press




Nello Cristianini, John Shawer-Taylor [2] 数据挖掘中的新方法-支持向量机 邓乃扬, 田英杰 [3] 机器学习. The book is titled Support Vector Machines and other Kernel Based Learning methods and is authored by Nello Cristianini and John-Shawe Taylor. 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]. This is because the only time the maximum margin hyperplane will change is if a new instance is introduced into the training set that is a support vectors. It has been shown to produce lower prediction error compared to classifiers based on other methods like artificial neural networks, especially when large numbers of features are considered for sample description. Their reproducibility was evaluated by an internal cross-validation method. The distinction between Toolboxes . Summary: Multivariate kernel-based pattern classification using support vector machines (SVM) with a novel modification to obtain more balanced sensitivity and specificity on unbalanced data-sets (i.e. Support Vector Machines (SVMs) are a technique for supervised machine learning. This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. 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. Many SPM users have created tools for neuroimaging analyses that are based on SPM . [1] An Introduction to Support Vector Machines and other kernel-based learning methods. It too is suited for an introduction to Support Vector Machines. You will find here a list of these tools classified between Toolboxes, Utilities, Batch Systems and Templates.