A sparse multinomial probit model for classification-Reference-Cited by-同舟云学术

A sparse multinomial probit model for classification

Published:2010-05-06 Issue:1 Volume:14 Page:47-55
ISSN:1433-7541
Container-title:Pattern Analysis and Applications
language:en
Short-container-title:Pattern Anal Applic

Author:

Ding Yunfei,Harrison Robert F.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition

Link

http://link.springer.com/content/pdf/10.1007/s10044-010-0177-7.pdf

Reference42 articles.

1. Mukherjee S (2003) Classifying microarray data using support vector machines. In: Berrar DP, Dubitzky W, Granzow M (eds) A practical approach to microarray data analysis. Kluwer, Boston

2. Weston J, Watkins C (1998) Multi-class support vector machines. Tech Rep CSD-TR-98-04, Department of Computer Science, Royal Holloway University of London

3. Lee Y, Lin Y, Wahba G (2001) Multicategory support vector machines. Tech Rep 1043, Department of Statistics, University of Wisconsin, Madison, WI

4. Harrison RF, Pasupa K, (2009) Sparse multinomial kernel discriminant analysis (sMKDA). Pattern Recognit 42:1795–1802

5. Abe S, (2007) Sparse least-squares support vector training in the reduced empirical feature space. Pattern Anal Appl 10:203–214

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Neural Connectivity Evolution during Adaptive Learning with and without Proprioception;Proceedings of the 7th International Conference on Movement and Computing;2020-07-05

2. Intentional signal in prefrontal cortex generalizes across different sensory modalities;Journal of Neurophysiology;2014-07-01

3. The Group Latent Variable Approach to Probit Binary Classifications;IEEE Transactions on Neural Networks and Learning Systems;2014-07