Robust Top- k Multiclass SVM for Visual Category Recognition-Reference-Cited by-同舟云学术

Robust Top- k Multiclass SVM for Visual Category Recognition

Published:2017-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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Author:

Chang Xiaojun¹,Yu Yao-Liang²,Yang Yi³

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA, USA

2. University of Waterloo, Waterloo, Canada

3. University of Technology Sydney, Sydney, Australia

Funder

Data to Decisions Cooperative Research Centre (www.d2dcrc.com.au)

NSERC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3097983.3097991

Reference32 articles.

1. Samy Bengio Jason Weston and David Grangier. 2010. Label Embedding Trees for Large Multi-Class Tasks. NIPS. Samy Bengio Jason Weston and David Grangier. 2010. Label Embedding Trees for Large Multi-Class Tasks. NIPS.

2. Antoine Bordes Léon Bottou Patrick Gallinari and Jason Weston 2007. Solving multiclass support vector machines with LaRank ICML. Antoine Bordes Léon Bottou Patrick Gallinari and Jason Weston 2007. Solving multiclass support vector machines with LaRank ICML.

3. On the algorithmic implementation of multiclass kernel-based vector machines;Crammer Koby;Journal of Machine Learning Research,2001

4. Jia Deng Jonathan Krause Alexander C. Berg and Fei-Fei Li 2012. Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition CVPR. Jia Deng Jonathan Krause Alexander C. Berg and Fei-Fei Li 2012. Hedging your bets: Optimizing accuracy-specificity trade-offs in large scale visual recognition CVPR.

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