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This paper present a principled SVM based speaker verification system. We propose a new framework and a new sequence kernel that can make use of any Mercer kernel at the frame level. An extension of the sequence kernel based on the Max operator is also pro ...
We introduce a family of box splines for efficient, accurate, and smooth reconstruction of volumetric data sampled on the body-centered cubic (BCC) lattice, which is the favorable volumetric sampling pattern due to its optimal spectral sphere packing prope ...
This paper presents the algorithms and results of our participation to the medical image annotation task of ImageCLEFmed 2007. We proposed, as a general strategy, a multi-cue approach where images are represented both by global and local descrip- tors, so ...
Recently, new sampling schemes were presented for signals with finite rate of innovation (FRI) using sampling kernels reproducing polynomials or exponentials. In this paper, we extend those sampling schemes to a distributed acquisition architecture in which ...
An efficient and general multiple kernel learning (MKL) algorithm has been recently proposed by \singleemcite{sonnenburg_mkljmlr}. This approach has opened new perspectives since it makes the MKL approach tractable for large-scale problems, by iteratively ...
An efficient and general multiple kernel learning (MKL) algorithm has been recently proposed by \singleemcite{sonnenburg_mkljmlr}. This approach has opened new perspectives since it makes the MKL approach tractable for large-scale problems, by iteratively ...
This paper present a principled SVM based speaker verification system. We propose a new framework and a new sequence kernel that can make use of any Mercer kernel at the frame level. An extension of the sequence kernel based on the Max operator is also pro ...
This thesis addresses text-independent speaker verification from a machine learning point of view. We use the machine learning framework to better define the problem and to develop new unbiased performance measures and statistical tests to compare objectiv ...
Every now and then, a new design of an interpolation kernel shows up in the literature. While interesting results have emerged, the traditional design methodology proves laborious and is riddled with very large systems of linear equations that must be solv ...
This paper presents a general method for incorporating prior knowledge into kernel methods. It applies when the prior knowledge can be formalized by the description of an object around each sample of the training set, assuming that all points in the given ...
