1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 A Hybrid Supervised ANN for Classification and Data Visualization Chee Siong Teh and Md. Sarwar Zahan Tapan Presented by Jun-Yi Wu 2010/09/28 2008 IEEE

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines  Motivation  Objectives  Methodology  Experiments  Conclusions  Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation  ANN usually do not support supervised classification and data visualization simultaneously.

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objectives  To propose a novel hybrid supervised ANN of Learning Vector Quantization(LVQ) with Adaptive Coordinate (AC) by hybridizing LVQ and modified AC approach.

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology A.Learning Vector Quantization(LVQ) B.Adaptive Coordinates(AC) C.Modified adaptation criteria of AC D.LVQ with AC Alogorithm E.Cost function of LVQ with AC 5

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Learning Vector Quantization(LVQ) 6

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Adaptive Coordinates(AC) 7

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Modified adaptation criteria of AC The proposed modification of AC is to ensure the integration of AC and LVQ enables the proposed method with data visualization. 8

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  LVQ with AC Alogorithm 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Cost function of LVQ with AC 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments A.Visualization evaluation using a 3D synthetic data set B.Visualization evaluation using WBC data set C.Visualization evaluation using Wine data set D.Quantitative evaluation of LVQ with AC’s visualization E.Classification evaluation F.Network utilization and dead neuron 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Visualization evaluation using a 3D synthetic data set 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 13

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Visualization evaluation using WBC data set 141 4

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Visualization evaluation using Wine data set 15

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Quantitative evaluation of LVQ with AC’s visualization 16

17. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Classification evaluation 17

18. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments  Network utilization and dead neuron 18

19. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion  Empirical studies show that LVQwithAC be able to provide promising classification simultaneously with effective visualizations. 19

20. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 20 Comments  Advantages ─ map. Applications ─ Classification