The Kernel Conjugate Gradient Algorithms

Kernel methods have been successfully applied to nonlinear problems in machine learning and signal processing. Various kernel-based algorithms have been proposed over the last two decades. In this paper, we investigate the kernel conjugate gradient (KCG) algorithms in both batch and online modes. By...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 66; H. 16; S. 4377 - 4387
Hauptverfasser:	Zhang, Ming, Wang, Xiaojian, Chen, Xiaoming, Zhang, Anxue
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 15.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive algorithms Algorithms Computational efficiency conjugate gradient algorithm Conjugates Convergence Criteria Hilbert space Iterative methods Kernel Machine learning Machine learning algorithms nonlinear processing online sparsification Optimization regularization Reproducing kernel Hilbert space Robustness (mathematics) Signal processing Signal processing algorithms
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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