A Survey on Potential of the Support Vector Machines in Solving Classification and Regression Problems

Luminita State (), Catalina Cocianu () and Doina Fusaru ()

Informatica Economica, 2010, vol. 14, issue 3, 128-139

Abstract: Kernel methods and support vector machines have become the most popular learning from examples paradigms. Several areas of application research make use of SVM approaches as for instance hand written character recognition, text categorization, face detection, pharmaceutical data analysis and drug design. Also, adapted SVM’s have been proposed for time series forecasting and in computational neuroscience as a tool for detection of symmetry when eye movement is connected with attention and visual perception. The aim of the paper is to investigate the potential of SVM’s in solving classification and regression tasks as well as to analyze the computational complexity corresponding to different methodologies aiming to solve a series of afferent arising sub-problems.

Keywords: Support Vector Machines; Kernel-Based Methods; Supervised Learning; Regression; Classification (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://revistaie.ase.ro/content/55/2002%20-%20Cata ... Luminita%20State.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:14:y:2010:i:3:p:128-139

Access Statistics for this article

Informatica Economica is currently edited by Ion Ivan

More articles in Informatica Economica from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC.
Bibliographic data for series maintained by Paul Pocatilu ().