Mon premier blog

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods John Shawe-Taylor, Nello Cristianini
Publisher: Cambridge University Press

PyML focuses on SVMs and other kernel methods. 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. Shogun - The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) . It is supported on Linux Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. 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]. 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. Nello Cristianini, John Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods 2000 | pages: 189 | ISBN: 0521780195. We follow the method introduced in [21] to solve this problem. Bpnn.py - Written by Neil Schemenauer, bpnn.py is used by an IBM article entitled "An introduction to neural networks". 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. 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.