This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2012 IEEE 12th International Conference on Data Mining Workshops
Nonlinear L-1 Support Vector Machines for Learning Using Privileged Information
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
Based on L-2 Support Vector Machines(SVMs), Vapnik and Vashist introduced the concept of Learning Using Privileged Information(LUPI). This new paradigm takes into account the elements of human teaching during the process of machine learning. However, with the utilization of privileged information, the extended L-2 SVM model given by Vapnik and Vashist doubles the number of parameters used in the standard L-2 SVM. Lots of computing time would be spent on tuning parameters. In order to reduce this workload, we proposed using L-1 SVM instead of L-2 SVM for LUPI in our previous work. Different from LUPI with L-2 SVM, which is formulated as quadratic programming, LUPI with L-1 SVM is essentially a linear programming and is computationally much cheaper. On this basis, we discuss how to employ the wisdom from teachers better and more flexible by LUPI with L-1 SVM in this paper. By introducing kernels, an extended L-1 SVM model, which is still a linear programming, is proposed. With the help of nonlinear kernels, the new model allows the privileged information be explored in a transformed feature space instead of the original data domain. Numerical experiment is carried out on both time series prediction and digit recognition problems. Experimental results also validate the effectiveness of our new method.
Index Terms:
Support vector machines,Kernel,Training,Machine learning,Mathematical model,Computational modeling,Time series analysis,binary classification,1-norm,support vector machine,kernel,privileged information
Citation:
Lingfeng Niu, Jianmin Wu, "Nonlinear L-1 Support Vector Machines for Learning Using Privileged Information," icdmw, pp.495-499, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
Usage of this product signifies your acceptance of the Terms of Use.