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

Informatică economică. 2010;14(3):128-139


Journal Homepage

Journal Title: Informatică economică

ISSN: 1453-1305 (Print); 1842-8088 (Online)

Publisher: Inforec Association

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware | Bibliography. Library science. Information resources

Country of publisher: Romania

Language of fulltext: Romanian; Moldavian; Moldovan, English

Full-text formats available: PDF



Luminita STATE
Catalina COCIANU


Double blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 8 weeks


Abstract | Full Text

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.