Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Multiclass boosting with repartitioning Graduate : Chen,

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Presentation transcript:

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Multiclass boosting with repartitioning Graduate : Chen, Shao-Pei Authors : Ling Li ICML

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 2 Motivation Objective Methodology AdaBoost.ECC AdaBoost.ERP Experimental Results Conclusion Outline

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objective  A new multi-class boosting algorithm that modifies the coding matrix according to the learning ability of the base learner.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Methodology-AdaBoost.ECC X AdaBoosting.ECCHamming distance Assign class T1: … T1:y3 T2:y2 … X1:y3 M SVM-Perceptron Code Book M TrainingTesting T

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Methodology-AdaBoost.ECC K-class, The training set contains N examples,, Where is the input and. Given an input x, the ensemble output W

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 The tangram experiment Max-cut Rand-half Maximize We have to find a good trade-off between maximizing and minimizing.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Methodology-AdaBoost.ERP X AdaBoost.ERP Assign class T1:y3 T2:y2 … Repartition Until convergence or some specified steps Hamming distance M’ M To reduce the cost. T1: … Code Book SVM-Perceptron AdaBoost.ERP Training Testing T

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Methodology-AdaBoost.ERP Repartition To reduce the cost.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Experimental Results

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