Support vector machines (SVM) can classify data sets along highly non-linear decision boundaries because of the kernel-trick. This expressiveness comes at a price: During test-time, the SVM classifier needs to compute the kernel innerproduct between a test sample and all support vectors. With large training data sets, the time required for this computation can be substantial. In this paper, we ...

A support vector machine constructs an optimal hyperplane from a small set of samples near the boundary. This makes it sensitive to these specific samples and tends to result in machines either too complex with poor generalization ability or too imprecise with high training error, depending on the kernel parameters. In this paper, we present an improved version of the method, called editing sup...

The linear support vector machine can be posed as a quadratic program in a variety of ways. In this paper, we look at a formulation using the two-norm for the misclassification error that leads to a positive definite quadratic program with a single equality constraint when the Wolfe dual is taken. The quadratic term is a small rank update to a positive definite matrix. We reformulate the optima...

A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a number of different locations with processing capabilities (nodes). In several examples, it has been found that a reasonably small amount of information is interchanged among nodes to obtain an SVM solution...

A new regression technique based on Vapnik’s concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend...

The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and regression problems. SVMs have gained widespread use in recent years because of successful applications like character recognition and the profound theoretical underpinnings concerning generalization performance. Yet, one of the remaining drawbacks of the SVM algorithm is its high computational dem...

Smoothing methods, extensively used for solving important mathematical programming problems and applications, are proposed here to generate and solve an unconstrained smooth reformulation of support vector machines for pattern classification using completely arbitrary kernels. We term such reformulations smooth support vector machines (SSVMs). A fast Newton-Armijo algorithm for solving the SSVM...

Existing Support Vector Machines (SVMs) need pre-wired finite time windows to predict and classify time series. They do not have an internal state necessary to deal with sequences involving arbitrary long-term dependencies. Here we introduce the first recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-bas...

This paper presents a classification algorithm based on Support Vector Machines classifiers combined with Boosting techniques. This classifier presents a better performance in training time, a similar generalization and a similar model complexity but the model representation is more compact.

In many problems of machine learning, the data are distributed nonlinearly. One way to address this kind of data is training a nonlinear classifier such as kernel support vector machine (kernel SVM). However, the computational burden of kernel SVM limits its application to large scale datasets. In this paper, we propose a Clustered Support Vector Machine (CSVM), which tackles the data in a divi...