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...

For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) ...

A novel approach for recognizing human activities with wearable sensors is investigated in this article. The key techniques of this approach include the generalized discriminant analysis (GDA) and the relevance vector machines (RVM). The feature vectors extracted from the measured signal are processed by GDA, with its dimension remarkably reduced from 350 to 12 while fully maintaining the most ...

A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions to the learning of decision surface. We cal...

In contrast to learning a general prediction rule, V. Vapnik proposed the transductive learning setting where predictions are made only at a fixed number of known test points. This allows the learning algorithm to exploit the location of the test points, making it a particular type of semi-supervised learning problem. Transductive support vector machines (TSVMs) implement the idea of transducti...

Many problems require making sequential decisions. For these problems, the benefit of acquiring further information must be weighed against the costs. In this paper, we describe the catenary support vector machine (catSVM), a margin-based method to solve sequential stopping problems. We provide theoretical guarantees for catSVM on future testing examples. We evaluated the performance of catSVM ...

Most Support Vector (SV) methods proposed in the recent literature can be viewed in a uni ed framework with great exibility in terms of the choice of the basis functions. We show that all these problems can be solved within a unique approach if we are equipped with a robust method for nding a sparse solution of a linear system. Moreover, for such a purpose, we propose an iterative algorithm tha...

An implicit Lagrangian for the dual of a simple reformulation of the standard quadratic program of a linear support vector machine is proposed. This leads to the minimization of an unconstrained differentiable convex function in a space of dimensionality equal to the number of classified points. This problem is solvable by an extremely simple linearly convergent Lagrangian support vector machin...

We introduce in this paper Fβ SVMs, a new parametrization of support vector machines. It allows to optimize a SVM in terms of Fβ , a classical information retrieval criterion, instead of the usual classification rate. Experiments illustrate the advantages of this approach with respect to the traditionnal 2norm soft-margin SVM when precision and recall are of unequal importance. An automatic mod...

By setting apart the two functions of a support vector machine: separation of points by a nonlinear surface in the original space of patterns, and maximizing the distance between separating planes in a higher dimensional space, we are able to deene indeenite, possibly discontinuous, kernels, not necessarily inner product ones, that generate highly nonlin-ear separating surfaces. Maximizing the ...