Seminar: Classification using One-class SVM under Lable Noise

Thesis Proposal Presentation

Hao Yuan
M.Sc. Thesis Proposal
Co-Supervisors: Dr. Jian Tang & Dr. Minglun Gong

Classification using One-class SVM under Lable Noise

Department of Computer Science
Friday, November 22, 2013, 11:40a.m., Room EN 2022


Abstract

The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. In classification area, there are two types of noise: feature noise and label noise. Modeling and dealing with these noises are important; however, there is not much literature
related to label noise. As the one-class SVM algorithm has advantage over standard binary SVM on handling ambiguous data, there is a strong indication that using One-class SVM can better handle label noise. In the research, we will analysis and model label noise, design one-class SVM based approach to deal with label noise, and try to apply this method to social network classification.

 

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