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
ISBN: 0521780195, 9780521780193
Publisher: Cambridge University Press
Format: chm
Page: 189

Cristianini, J.S.Taylor (2000), An Introduction to Support Vector Machine and Other Kernel-Based Learning Methods, Cambridge Press University. 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. Support Vector Machine (SVM) is a supervised learning algorithm developed by Vladimir Vapnik and his co-workers at AT&T Bell Labs in the mid 90's. In simple words, given a set of training examples, each marked as belonging to one of two categories, a SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Support Vector However, modifications had been based on GPL code by Sylvain Roy. Kernel methods in general have gained increased attention in recent years, partly due to the grown of popularity of the Support Vector Machines. Bounds the influence of any single point on the decision boundary, for derivation, see Proposition 6.12 in Cristianini/Shaw-Taylor's "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods". 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. Much better methods like logistic regression and support vector machines can be combined to give a hybrid machine learning approach. 515/, An introduction to support Vector Machines: and other kernel-based learning methods, N Cristianini… - 2000 - books.google.com. Publisher: Cambridge University Press; 1 edition Language: English ISBN: 0521780195 Paperback: 189 pages Data: March 28, 2000 Format: CHM Description: free Download not from rapidshare or mangaupload.