Results 1  10
of
267
LIBSVM: A library for support vector machines,”
 ACM Transactions on Intelligent Systems and Technology,
, 2011
"... Abstract LIBSVM is a library for support vector machines (SVM). Its goal is to help users to easily use SVM as a tool. In this document, we present all its implementation details. For the use of LIBSVM, the README file included in the package and the LIBSVM FAQ provide the information. ..."
Abstract

Cited by 6491 (84 self)
 Add to MetaCart
(Show Context)
Abstract LIBSVM is a library for support vector machines (SVM). Its goal is to help users to easily use SVM as a tool. In this document, we present all its implementation details. For the use of LIBSVM, the README file included in the package and the LIBSVM FAQ provide the information.
A tutorial on support vector regression
, 2004
"... In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing ..."
Abstract

Cited by 867 (3 self)
 Add to MetaCart
In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
Abstract

Cited by 781 (29 self)
 Add to MetaCart
Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a preliminary theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled d...
An introduction to kernelbased learning algorithms
 IEEE TRANSACTIONS ON NEURAL NETWORKS
, 2001
"... This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and ..."
Abstract

Cited by 598 (55 self)
 Add to MetaCart
This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and
Multiple kernel learning, conic duality, and the SMO algorithm
 In Proceedings of the 21st International Conference on Machine Learning (ICML
, 2004
"... While classical kernelbased classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimiz ..."
Abstract

Cited by 444 (31 self)
 Add to MetaCart
(Show Context)
While classical kernelbased classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to a convex optimization problem known as a quadraticallyconstrained quadratic program (QCQP). Unfortunately, current convex optimization toolboxes can solve this problem only for a small number of kernels and a small number of data points; moreover, the sequential minimal optimization (SMO) techniques that are essential in largescale implementations of the SVM cannot be applied because the cost function is nondifferentiable. We propose a novel dual formulation of the QCQP as a secondorder cone programming problem, and show how to exploit the technique of MoreauYosida regularization to yield a formulation to which SMO techniques can be applied. We present experimental results that show that our SMObased algorithm is significantly more efficient than the generalpurpose interior point methods available in current optimization toolboxes. 1.
Working set selection using second order information for training SVM
 Journal of Machine Learning Research
"... Working set selection is an important step in decomposition methods for training support vector machines (SVMs). This paper develops a new technique for working set selection in SMOtype decomposition methods. It uses second order information to achieve fast convergence. Theoretical properties such ..."
Abstract

Cited by 285 (12 self)
 Add to MetaCart
Working set selection is an important step in decomposition methods for training support vector machines (SVMs). This paper develops a new technique for working set selection in SMOtype decomposition methods. It uses second order information to achieve fast convergence. Theoretical properties such as linear convergence are established. Experiments demonstrate that the proposed method is faster than existing selection methods using first order information.
Less is more: Active learning with support vector machines
, 2000
"... We describe a simple active learning heuristic which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification tasks. We observe a number of benefits, the most surprising of which is that a SVM trained on a wellchosen subset of the av ..."
Abstract

Cited by 278 (1 self)
 Add to MetaCart
(Show Context)
We describe a simple active learning heuristic which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification tasks. We observe a number of benefits, the most surprising of which is that a SVM trained on a wellchosen subset of the available corpus frequently performs better than one trained on all available data. The heuristic for choosing this subset is simple to compute, and makes no use of information about the test set. Given that the training time of SVMs depends heavily on the training set size, our heuristic not only offers better performance with fewer data, it frequently does so in less time than the naive approach of training on all available data. 1.
A dual coordinate descent method for largescale linear SVM.
 In ICML,
, 2008
"... Abstract In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such largescale sparse data. This paper presents a novel dual coordinate descent method for linear SVM with L1and L2l ..."
Abstract

Cited by 207 (19 self)
 Add to MetaCart
(Show Context)
Abstract In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such largescale sparse data. This paper presents a novel dual coordinate descent method for linear SVM with L1and L2loss functions. The proposed method is simple and reaches an accurate solution in O(log(1/ )) iterations. Experiments indicate that our method is much faster than state of the art solvers such as Pegasos, TRON, SVM perf , and a recent primal coordinate descent implementation.
Training Invariant Support Vector Machines
, 2002
"... Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide ..."
Abstract

Cited by 186 (16 self)
 Add to MetaCart
Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide experimental results, and discuss their respective merits. One of the significant new results reported in this work is our recent achievement of the lowest reported test error on the wellknown MNIST digit recognition benchmark task, with SVM training times that are also significantly faster than previous SVM methods.
The analysis of decomposition methods for support vector machines
 IEEE Transactions on Neural Networks
, 1999
"... Abstract. The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for boundconstrained SVM formulations we demonstrate that the w ..."
Abstract

Cited by 134 (21 self)
 Add to MetaCart
Abstract. The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for boundconstrained SVM formulations we demonstrate that the working set selection is not a trivial task. Then from the experimental analysis we propose a simple selection of the working set which leads to faster convergences for difficult cases. Numerical experiments on different types of problems are conducted to demonstrate the viability of the proposed method.