Idioma:

Multi-task Learning for one-class classification

Haiqin Yang ; King, Irwin ; Lyu, Michael R

The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, p.1-8

Texto completo disponível

Citações Citado por

Título:
Multi-task Learning for one-class classification
Autor: Haiqin Yang ; King, Irwin ; Lyu, Michael R
É parte de: The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, p.1-8
Descrição: In this paper, we address the problem of one-class classification. Taking into account the fact that in some applications, the given training samples are rather limited, we attempt to utilize the advantages of Multi-task Learning (MTL), where the data of related tasks may share similar structure and helpful information. We then propose an MTL framework for one-class classification. The framework derives from the one-class v-SVM and makes use of related tasks by constraining them to have similar solutions. This formulation can be cast into a second-order cone program, which achieves a global solution and is solved efficiently. Further, the framework also maintains the favorable property of the v parameter in the v-SVM, which can control the fraction of outliers and support vectors, in one-class classification. This framework also connects with several existing models. Experimental results on both synthetic and real-world datasets demonstrate the properties and advantages of our proposed model.
Editor: IEEE
Idioma: Inglês

Voltar para lista de resultados