Doubly random parallel stochastic methods for large scale learning

We consider learning problems over training sets in which both, the number of training examples and the dimension of the feature vectors, are large. To solve these problems we propose the random parallel stochastic algorithm (RAPSA). We call the algorithm random parallel because it utilizes multiple...

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Vydané v:	Proceedings of the American Control Conference s. 4847 - 4852
Hlavní autori:	Mokhtari, Aryan, Koppel, Alec, Ribeiro, Alejandro
Médium:	Konferenčný príspevok.. Journal Article
Jazyk:	English
Vydavateľské údaje:	American Automatic Control Council (AACC) 01.07.2016
Predmet:	Algorithms Convergence Learning Linear programming Mathematical analysis Optimization Parallel processing Processors Program processors Radio frequency Stochasticity Training Vectors (mathematics)
ISSN:	2378-5861
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