Generalization bounds for sparse random feature expansions

Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach to standard neural networks since they can represent similar function spaces without a costly training phase. However,...

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Veröffentlicht in:	Applied and computational harmonic analysis Jg. 62; S. 310 - 330
Hauptverfasser:	Hashemi, Abolfazl, Schaeffer, Hayden, Shi, Robert, Topcu, Ufuk, Tran, Giang, Ward, Rachel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.01.2023
Schlagworte:	Compressive sensing Generalization error Random features Sparse optimization Sparse optimization Compressive sensing Random features 60B20 Generalization error 65D15 68Q32 46N10
ISSN:	1063-5203, 1096-603X
Online-Zugang:	Volltext
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