6 pagesInternational audienceThis Note proposes a new methodology for function classification with Support Vector Machine (SVM). Rather than relying on projection on a truncated Hilbert basis as in our previous work, we use an implicit spline interpolation that allows us to compute SVM on the derivatives of the studied functions. To that end, we propose a kernel defined directly on the discretizations of the observed functions. We show that this method is universally consistent
In this paper, we introduce a new kernel function for improving the accuracy of the Support Vector M...
Abstract. Support vector machines (SVMs) appeared in the early nineties as optimal margin classifier...
In this paper, we introduce a set of new kernel functions derived from the generalized Legendre poly...
6 pagesInternational audienceThis Note proposes a new methodology for function classification with S...
International audienceIn many applications, input data are in fact sampled functions rather than sta...
In this work, we provide an exposition of the support vector machine classifier (SVMC) algorithm. We...
International audienceDans un nombre croissant d'applications, les données statistiques ne sont plus...
First and foremost, I am greatly indebted to the supervisor of this work, Prof. Amir Atiya, for his ...
Abstract: In this paper we introduce a new kernel function that could improve the SVMs classificati...
Abstract. Support vector machine (SVM) is a very popular method for bi-nary data classification in d...
13 pagesInternational audienceIn many applications, input data are sampled functions taking their va...
Abstract. In many applications, input data are in fact sampled functions rather than standard high d...
Absiracf-One of the uses of the support vector machine (SVM), as introduced in [ll, is as a function...
We study computational issues for support vector classification with penalised spline kernels. We sh...
We study computational issues for support vector classification with penalised spline kernels. We sh...
In this paper, we introduce a new kernel function for improving the accuracy of the Support Vector M...
Abstract. Support vector machines (SVMs) appeared in the early nineties as optimal margin classifier...
In this paper, we introduce a set of new kernel functions derived from the generalized Legendre poly...
6 pagesInternational audienceThis Note proposes a new methodology for function classification with S...
International audienceIn many applications, input data are in fact sampled functions rather than sta...
In this work, we provide an exposition of the support vector machine classifier (SVMC) algorithm. We...
International audienceDans un nombre croissant d'applications, les données statistiques ne sont plus...
First and foremost, I am greatly indebted to the supervisor of this work, Prof. Amir Atiya, for his ...
Abstract: In this paper we introduce a new kernel function that could improve the SVMs classificati...
Abstract. Support vector machine (SVM) is a very popular method for bi-nary data classification in d...
13 pagesInternational audienceIn many applications, input data are sampled functions taking their va...
Abstract. In many applications, input data are in fact sampled functions rather than standard high d...
Absiracf-One of the uses of the support vector machine (SVM), as introduced in [ll, is as a function...
We study computational issues for support vector classification with penalised spline kernels. We sh...
We study computational issues for support vector classification with penalised spline kernels. We sh...
In this paper, we introduce a new kernel function for improving the accuracy of the Support Vector M...
Abstract. Support vector machines (SVMs) appeared in the early nineties as optimal margin classifier...
In this paper, we introduce a set of new kernel functions derived from the generalized Legendre poly...