Site Meter Abstract of "Multicategory Incremental Proximal Support Vector Classifiers"


"Multicategory Incremental Proximal Support Vector Classifiers"

Authors: Amund Tveit, Magnus Lie Hetland and Håvard Engum

Support Vector Machines (SVMs) are an efficient data mining approach for classification, clustering and time series analysis. In recent years, a tremendous growth in the amount of data gathered has changed the focus of SVM classifier algorithms from providing accurate results to enabling incremental (and decremental) learning with new data (or unlearning old data) without the need for computationally costly retraining with the old data. In this paper we propose an efficient algorithm for multicategory classification with the incremental proximal SVM introduced by Fung and Mangasarian.

Implementation of Algorithms: Implementations can be found at the Sourceforge Incridge - A Scalable Classification Tool project page.


Known Citations:
  1. Matteo Roffilli. "Advanced Machine Learning Techniques for Digital Mammography", PhD Thesis, University of Bologna, Padova, Italy, March 2006

  2. Shibin Qiu and Terran Lane, Parallel Kernel Computation for High Dimensional Data and Its Application to fMRI Image Classification, University of New Mexico Technical Report TR-CS-2004-12, USA, December, 2003

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