Optimizing Top- k Multiclass SVM via Semismooth Newton Algorithm

Top- k performance has recently received increasing attention in large data categories. Advances, like a top- k multiclass support vector machine (SVM), have consistently improved the top- k accuracy. However, the key ingredient in the state-of-the-art optimization scheme based upon stochastic du...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2018-12, Vol.29 (12), p.6264-6275
Main Authors: Chu, Dejun, Lu, Rui, Li, Jin, Yu, Xintong, Zhang, Changshui, Tao, Qing
Format: Article
Language:English
Subjects:
Fasteners
Learning systems
Multiclass support vector machine (SVM)
Newton method
Optimization
root finding
semismooth Newton method
Sorting
stochastic dual coordinate ascent (SDCA) optimization
Support vector machines
top-k error
Training
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