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Performance analysis of LVQ algorithms A statistical physics approach

Performance analysis of LVQ algorithms A statistical physics approach. Presenter : Jiang-Shan Wang Authors : Anarta Ghosh, Michael Biehl, Barbara Hammer. 國立雲林科技大學 National Yunlin University of Science and Technology. NN 2006. Outline. Motivation Objective Method Experiment

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Performance analysis of LVQ algorithms A statistical physics approach

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  1. Performance analysis of LVQ algorithms A statistical physics approach Presenter : Jiang-Shan Wang Authors : Anarta Ghosh, Michael Biehl, Barbara Hammer 國立雲林科技大學 National Yunlin University of Science and Technology NN 2006

  2. Outline • Motivation • Objective • Method • Experiment • Conclusion • Comments

  3. Motivation • The exact dynamics as well as the generalization ability of many LVQ algorithms have not been thoroughly investigated so far.

  4. Objective To analyze performance of LVQ algorithms.

  5. Method • LVQ-type algorithms: • LVQ • LVQ 2.1 • LFM • LVQ+ • VQ

  6. Method-LVQ

  7. Method-LVQ 2.1

  8. Method-LFM Robust soft learning vector quantization (RSLVQ) results from an optimization of a cost function which considers the ratio of the class distribution and unlabeled data distribution.

  9. Experiment

  10. Experiment

  11. Conclusion The main goal is to provide a deterministic description of the stochastic evolution of the learning process in an exact mathematical way for interesting learning rules, which will be helpful in constructing efficient LVQ algorithms.

  12. Comments • Advantage • Many analysts. • Drawback • Too theoretical to study. • Application

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