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Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks.

M. Sugiyama, and H. Ogawa. NIPS, page 624-630. The MIT Press, (1999)

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Partial Projection Filter for Signal Restoration in the Presence of Signal Space Noise.A. Syed, and H. Ogawa. IEICE Transactions, 87-D (12): 2828-2836 (2004)Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks.M. Sugiyama, and H. Ogawa. NIPS, page 624-630. The MIT Press, (1999)Incremental Active Learning for Optimal Generalization.M. Sugiyama, and H. Ogawa. Neural Computation, 12 (12): 2909-2940 (2001)Relative Karhunen-Loève transform.Y. Yamashita, and H. Ogawa. IEEE Trans. Signal Processing, 44 (2): 371-378 (1996)Error correcting memorization learning for noisy training examples.A. Nakashima, A. Hirabayashi, and H. Ogawa. Neural Networks, 14 (1): 79-92 (2001)Relative Karhunen-Loeve operator.Y. Yamashita, and H. Ogawa. ICPR (3), page 168-170. IEEE, (1994)What Can We See behind Sampling Theorems?H. Ogawa. IEICE Transactions, 92-A (3): 688-695 (2009)A New Modulated Hebbian Learning Rule - Biologically Plausible Method for Local Computation of a Principal Subspace.M. Jankovic, and H. Ogawa. Int. J. Neural Syst., 13 (4): 215-223 (2003)A functional analytic approach to incremental learning in optimally generalizing neural networks.S. Vijayakumar, and H. Ogawa. ICNN, page 777-782. IEEE, (1995)Modulated Hebb-Oja learning Rule-a method for principal subspace analysis.M. Jankovic, and H. Ogawa. IEEE Trans. Neural Networks, 17 (2): 345-356 (2006)

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Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks.

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Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks.