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20,892
On the convergence of a modified version of SVM light algorithm
 Optimization Methods and Software
, 2005
"... In this work we consider the convex quadratic programming problem arising in Support Vector Machine (SVM), which is a technique designed to solve a variety of learning and pattern recognition problems. Since the Hessian matrix is dense and real applications lead to large scale problems, several deco ..."
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Cited by 10 (1 self)
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decomposition methods have been proposed, that split the original problem into a sequence of smaller subproblems. SVM light algorithm is a commonly used decomposition method for SVM, and its convergence has been proved only recently under a suitable blockwise convexity assumption on the objective function
Making LargeScale SVM Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 1860 (17 self)
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learning tasks with many training examples, offtheshelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SV M light1 is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic
Making LargeScale Support Vector Machine Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 628 (1 self)
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learning tasks with many training examples, offtheshelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVM light1 is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents
Training Linear SVMs in Linear Time
, 2006
"... Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like text classification, wordsense disambiguation, and drug design. These applications involve a large number of examples n ..."
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Cited by 549 (6 self)
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is based on an alternative, but equivalent formulation of the SVM optimization problem. Empirically, the CuttingPlane Algorithm is several orders of magnitude faster than decomposition methods like SVMLight for large datasets.
A Novel Approach for Combating Spamdexing in Web using UCINET and SVM Light
, 2011
"... ABSTRACTSearch Engine spam is a web page or a portion of a web page which has been created with the intention of increasing its ranking in search engines. Web spamming refers to actions intended to mislead search engines and give some pages higher ranking than they deserve. Anyone who uses a search ..."
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Cited by 1 (0 self)
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detection that uses machine learning as a means for detecting spam. This new approach uses UCINET software and a series of content combined with a Support Vector Machine (SVM) Binary classifier to determine if a given webpage is spam. The link farm can identify based on degree, betweenness and Eigen vector
A tutorial on support vector machines for pattern recognition
 Data Mining and Knowledge Discovery
, 1998
"... The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and nonseparable data, working through a nontrivial example in detail. We describe a mechanical analogy, and discuss when SV ..."
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Cited by 3390 (12 self)
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SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very
SVMTorch: Support Vector Machines for LargeScale Regression Problems
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2001
"... Support Vector Machines (SVMs) for regression problems are trained by solving a quadratic optimization problem which needs on the order of l 2 memory and time resources to solve, where l is the number of training examples. In this paper, we propose a decomposition algorithm, SVMTorch 1 , whic ..."
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Cited by 314 (10 self)
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, which is similar to SVMLight proposed by Joachims (1999) for classification problems, but adapted to regression problems. With this algorithm, one can now efficiently solve largescale regression problems (more than 20000 examples). Comparisons with Nodelib, another publicly available SVM algorithm
CuttingPlane Training of Structural SVMs
, 2007
"... Discriminative training approaches like structural SVMs have shown much promise for building highly complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. However, current training algorithms are computationally expensive or i ..."
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Cited by 321 (10 self)
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tagging, and CFG parsing. The experiments show that the cuttingplane algorithm is broadly applicable and fast in practice. On large datasets, it is typically several orders of magnitude faster than conventional training methods derived from decomposition methods like SVM light, or conventional cutting
Learning methods for generic object recognition with invariance to pose and lighting
 In Proceedings of CVPR’04
, 2004
"... We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniformcolored toys under 36 angles, 9 azimuths, and 6 lighting co ..."
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Cited by 255 (18 self)
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We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniformcolored toys under 36 angles, 9 azimuths, and 6 lighting
Results 1  10
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20,892