Skip to Main content Skip to Navigation
Conference papers

F-SVR: A new learning algorithm for support vector regression

Abstract : In this paper, we present a new method for optimizing support vector machines for regression problems. This algorithm searches for efficient feasible directions. Within these selected directions, we choose the best one, i.e. the one, coupled with an optimal step analytical evaluation, that ensures a maximum increase of the objective function. The resulting solution, the gradient and the objective function are recursively determined and the Gram matrix has not to be stored. Our algorithm is based on SVM-Torch proposed by Collobert for regression, which is similar to SVM-Light suggested by Joachims for classifications problems, but adapted to regression problems. We are also inspired by LASVM proposed by Bordes for classification problems. F-SVR algorithm uses a new efficient working set selection heuristic, ingeniously exploits quadratic function properties, so it is fast as well as easy to implement and is able to perform on large data sets.
Document type :
Conference papers
Complete list of metadatas

https://hal-utt.archives-ouvertes.fr/hal-02316564
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Tuesday, October 15, 2019 - 1:59:27 PM
Last modification on : Wednesday, October 16, 2019 - 1:31:20 AM

Identifiers

Collections

CNRS | ROSAS | UTT

Citation

Mireille Tohmé, Régis Lengellé. F-SVR: A new learning algorithm for support vector regression. ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2008, Las Vegas, United States. pp.2005-2008, ⟨10.1109/ICASSP.2008.4518032⟩. ⟨hal-02316564⟩

Share

Metrics

Record views

20