Optimizing Kernel Machines Using Deep Learning

Building highly nonlinear and nonparametric models is central to several state-of-the-art machine learning systems. Kernel methods form an important class of techniques that induce a reproducing kernel Hilbert space (RKHS) for inferring non-linear models through the construction of similarity functi...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2018-11, Vol.29 (11), p.5528-5540
Hauptverfasser:	Huan Song, Thiagarajan, Jayaraman J., Sattigeri, Prasanna, Spanias, Andreas
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Case studies Computational modeling Computer applications Computer architecture Computing time Construction Data models Deep learning Deep neural networks (DNNs) Hilbert space Kernel kernel methods Learning algorithms Machine learning MATHEMATICS AND COMPUTING multiple kernel learning (MKL) Neural networks Nyström approximation Optimization Regularization Subspaces Task analysis Training
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