Ultra-Sparse Classifiers Through Minimizing the VC Dimension in the Empirical Feature Space-Reference-Cited by-同舟云学术

Ultra-Sparse Classifiers Through Minimizing the VC Dimension in the Empirical Feature Space

Published:2018-02-17 Issue:2 Volume:48 Page:881-913
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Jayadeva ,Sharma Mayank,Soman Sumit,Pant Himanshu

Funder

Microsoft Chair Professor Project Grant

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

http://link.springer.com/article/10.1007/s11063-018-9793-9/fulltext.html

Reference66 articles.

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4. Bach FR, Jordan MI (2005) Predictive low-rank decomposition for kernel methods. In: Proceedings of the 22nd international conference on machine learning, ACM, pp 33–40

5. Blake C, Keogh E, Merz CJ (1998) UCI repository of machine learning databases. University of California, Irvine. http://www.ics.uci.edu/~mlearn/MLRepo sitory.html

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