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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Veröffentlicht in:	Proceedings of the American Control Conference S. 4847 - 4852
Hauptverfasser:	Mokhtari, Aryan, Koppel, Alec, Ribeiro, Alejandro
Format:	Tagungsbericht Journal Article
Sprache:	Englisch
Veröffentlicht:	American Automatic Control Council (AACC) 01.07.2016
Schlagworte:	Algorithms Convergence Learning Linear programming Mathematical analysis Optimization Parallel processing Processors Program processors Radio frequency Stochasticity Training Vectors (mathematics)
ISSN:	2378-5861
Online-Zugang:	Volltext
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