1. Merter Sualp and Tolga Can IEEE Transactions on Knowledge and Data Engineering 1 Paper study- 2012/12/22

2. OUTLINE 2 Introduction Method of DiVo Results Discussion

3. Introduction 3 When there exist sufficiently many training examples, the estimation error of the model tends to decrease. Although, it may not be possible or feasible to collect sufficient training data, especially in application domains. Negative training data is artificially generated. fidelity Methods which are specifically developed to work with one class training datasets bypass the artificial data generation stage.

4. Method of DiVo 4

5. Method of DiVo - training 5

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7. Method of DiVo - testing 7

8. Results 8 We simulate the one class classification problem by selecting each class as the target class and the rest of them as the non- targets and using a subset of the target class samples during the training phase.

9. Results 9

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11. Discussion 11 DiVo-M

12. Discussion 12 DiVo-E

13. Discussion 13 Biomed Data ( 藍 )

14. Discussion 14 Dermatology Data ( 黃 )