Acceleration for proximal stochastic dual coordinate ascent algorithm in solving regularised loss minimisation with ${ \bf \ell _2}$ℓ2 norm

An accelerated version of the proximal stochastic dual coordinate ascent (SDCA) algorithm in solving regularised loss minimisation with ${\bf {\ell _2}}$ℓ2 norm is presented, wherein a momentum is introduced and the strong theoretical guarantees of SDCA are shared. Moreover, it is also suitable for...

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Bibliographic Details
Published in:Electronics letters 2018-03, Vol.54 (5), p.315-317
Main Authors: Tan, Beibei, Liu, Benyong
Format: Article
Language:English
Subjects:
learning (artificial intelligence)
logistic regression
machine learning optimisation problems
minimisation
multiclass SVM
proximal stochastic dual coordinate ascent algorithm
regression analysis
regularised loss minimisation
ridge regression
SDCA
Signal processing
support vector machine
support vector machines
Online Access:Get full text
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