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
Page: 189
Format: chm
Publisher: Cambridge University Press

CRISTIANINI, N.; SHAWE-TAYLOR, J. Shawe-Taylor & Christianini (2004). 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. 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. Kernel Methods for Pattern Analysis . Shawe-Taylor, An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods, Cambridge University Press, New York, NY, 2000. An Introduction to Support Vector Machines and other kernel-based learning methods . We follow the method introduced in [21] to solve this problem. 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]. 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. 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. The subsequent predictive models are trained with support vector machines introducing the variables sequentially from a ranked list based on the variable importance. Christianini & Shawe-Taylor (2000). In this work, we provide extended details of our methodology and also present analysis that tests the performance of different supervised machine learning methods and investigates the discriminative influence of the proposed features. Cambridge: Cambridge University Press, 2000. An Introduction to Support Vector Machines and other kernel-based learning methods.