Sample size selection in optimization methods for machine learning

This paper presents a methodology for using varying sample sizes in batch-type optimization methods for large-scale machine learning problems. The first part of the paper deals with the delicate issue of dynamic sample selection in the evaluation of the function and gradient. We propose a criterion...

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Veröffentlicht in:	Mathematical programming Jg. 134; H. 1; S. 127 - 155
Hauptverfasser:	Byrd, Richard H., Chin, Gillian M., Nocedal, Jorge, Wu, Yuchen
Format:	Journal Article Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer-Verlag 01.08.2012 Springer Springer Nature B.V
Schlagworte:	Algorithms Alliances Applied sciences Approximation Calculus of variations and optimal control Calculus of Variations and Optimal Control; Optimization Combinatorics Datasets Exact sciences and technology Full Length Paper Input output Machine learning Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Methods Numerical Analysis Operational research and scientific management Operational research. Management science Optimization Optimization algorithms Sample size Sample variance Sampling techniques Sciences and techniques of general use Studies Theoretical Voice recognition 65K05 49M15 49M37 Costs Sample size Iterative method Non linear programming Lot sizing Dimensioning Function evaluation Vector space Learning (artificial intelligence) Batch process Speech recognition Batch production Sampling Newton method Gradient method Mathematical programming Large scale system
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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