OATAO - Open Archive Toulouse Archive Ouverte Open Access Week

Self Adaptive Support Vector Machine: A Multi-Agent Optimization Perspective

Couellan, Nicolas and Jan, Sophie and Jorquera, Tom and Georgé, Jean-Pierre Self Adaptive Support Vector Machine: A Multi-Agent Optimization Perspective. (2015) Expert systems with Applications, 42 (9). 4284-4298. ISSN 0957-4174

[img]
Preview
(Document in English)

PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
1MB

Official URL: http://dx.doi.org/10.1016/j.eswa.2015.01.028

Abstract

Support Vector Machines (SVM) have been in the forefront of machine learning research for many years now. They have very nice theoretical properties and have proven to be efficient in many real life applications but the design of SVM training algorithms often gives rise to challenging optimization issues. We propose here to review the basics of Support Vector Machine learning from a multi-agent optimization perspective. Multi-agents systems break down complex optimization problems into elementary “oracle” tasks and perform a collaborative solving process resulting in a self-organized solution of the complex problems. We show how the SVM training problem can also be “tackled” from this point of view and provide several perspectives for binary classification, hyperparameters selection, multiclass learning as well as unsupervised learning. This conceptual work is illustrated through simple examples in order to convey the ideas and understand the behavior of agent cooperation. The proposed models provide simple formulations of complex learning tasks that are sometimes very difficult to solve with classical optimization strategies. The ideas that are discussed open up perspectives for the design of new distributed cooperative learning systems.

Item Type:Article
Additional Information:Thanks to Elsevier editor. The definitive version is available at http://www.sciencedirect.com The original PDF of the article can be found at Expert systems with Applications (ISSN 0957-4174) website : http://www.sciencedirect.com/science/article/pii/S0957417415000433
HAL Id:hal-01387802
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Université de Toulouse > Institut National des Sciences Appliquées de Toulouse - INSA (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UPS (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Statistics:download
Deposited By: IRIT IRIT
Deposited On:06 Oct 2016 15:44

Repository Staff Only: item control page