1 / 11

Multiclass boosting with repartitioning

Multiclass boosting with repartitioning. Graduate : Chen, Shao-Pei Authors : Ling Li ICML. Outline. Motivation Objective Methodology AdaBoost.ECC AdaBoost.ERP Experimental Results Conclusion. 2. Motivation.

eramey
Télécharger la présentation

Multiclass boosting with repartitioning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Multiclass boosting with repartitioning Graduate : Chen, Shao-Pei Authors : Ling Li ICML

  2. Outline • Motivation • Objective • Methodology • AdaBoost.ECC • AdaBoost.ERP • Experimental Results • Conclusion 2

  3. Motivation The quality of the final solution is affected by both the performance of the base learner and the error-correcting ability of the coding matrix. A coding matrix with strong error-correcting ability may not be overall optimal. 3

  4. Objective • A new multi-class boosting algorithm that modifies the coding matrix according to the learning ability of the base learner.

  5. Methodology-AdaBoost.ECC X Training Testing T SVM-Perceptron Code Book AdaBoosting.ECC Hamming distance Assign class T1: -1-1-11111 … M X1:y3 T1:y3 T2:y2 … M

  6. Methodology-AdaBoost.ECC W K-class, The training set contains N examples, , Where is the input and . Given an input x, the ensemble output

  7. The tangram experiment Max-cut Maximize Rand-half We have to find a good trade-off between maximizing and minimizing .

  8. Methodology-AdaBoost.ERP Until convergence or some specified steps SVM-Perceptron AdaBoost.ERP Repartition M M’ Training Testing AdaBoost.ERP Assign class T X T1:y3 T2:y2 … Hamming distance Code Book To reduce the cost. T1: -1-1-11111 …

  9. Methodology-AdaBoost.ERP Repartition To reduce the cost.

  10. Experimental Results

  11. Conclusion • The improvement can be especially significant when the base learner is not very powerful. • Compared to boosting algorithms, their training time is usually much less, and be comparable or even lower.

More Related