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

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  • Luminita STATE
  • Catalina COCIANU
  • Doina FUSARU

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.

Suggested Citation

  • Luminita STATE & Catalina COCIANU & Doina FUSARU, 2010. "A Survey on Potential of the Support Vector Machines in Solving Classification and Regression Problems," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 14(3), pages 128-139.
    • Handle: RePEc:aes:infoec:v:14:y:2010:i:3:p:128-139
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    References listed on IDEAS

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    1. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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