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"Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients"

Authors: Amund Tveit, Magnus Lie Hetland and Håvard Engum

Abstract:
This paper presents an efficient approach for supporting decremental learning for incremental proximal support vector machines (SVM). The presented decremental algorithm based on decay coefficients is compared with an existing window-based decremental algorithm, and is shown to perform at a similar level in accuracy, but providing significantly better computational performance.

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

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

  2. Jianpei Zhang, Zhongwei Li and Jing Yang. "A divisional incremental training algorithm of support vector machine", Proceedings of IEEE conference on Mechatronics and Automation, IEEE, Niagara Falls, Canada, 2005.

  3. Dai Liu-ling Dai, Li Xue-mei, Huang He-yan and Chen Zhao-xiong. "An On-line Text Categorization Algorithm Based On Information Fusion: Semantic SVM", Beijing, China.

  4. Jian-Pei Zhang, Zhong-Wi Li, Jing Yang and Yuan Li. "A Gradual Training Algorithm of Incremental Support Vector Machine Learning", Proceedings of the 1st International Conference on Advances in Natural Computation, Lecture Notes in Computer Science 3610 (LNCS), Springer-Verlag, Changsha, China, August 2005

  5. Jing Yang, Zhong-Wei Li and Jian-Pei Zhang. "A Training Algorithm on Incremental Support Vector Machine with Recombining Method", Proceedings of the 4th International Conference on Machine Learning and Cybernetics, Guangzhou, China, August 2005

  6. Achim Rettinger. "Opponent-Adaptive Online Multiagent Learning: Concept-Recall with Expert Ensembles for Simulated-Soccer-Agents", Diploma Thesis, University of Koblenz, Germany, 2005

  7. Herrn Daniel Schneegass. "DoubleMaxMinOver Approach zur Bestimmung der Support Vektoren bei Klassifikation und Regression mit Anwendungen in der Prozessindustrie", Diplomarbeit, Institute for Neuro and Bioinformatik, University of Lubeck, Germany, 2005

  8. Y. Chen and F. Naghdy. "Acquisition of manipulation skills from a haptic rendered virtual environment", Journal of Integrated Computer-Aided Engineering, IOS Press, 11(4), pp 359--372, The Netherlands, 2004

  9. "The Semantic SVM Algorithm for Text Categorization and its On-line Learning Algorithm", Computer Engineering and Applications, Volume 40, Number 36, pp 11-14, 2004
  10. Hyunsoo Kim and Haesun Park, Incremental and decremental least squares support vector machine and its application to drug design, Proceedings of the IEEE Computer Society Bioinformatics Conference (CSB2004), p656-657, Stanford, CA, August, 16-19, USA, 2004.

  11. 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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