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A deep manifold learning approach for spatial-spectral classification with limited labeled training samples.

X. Zhou, N. Liu, F. Tang, Y. Zhao, K. Qin, L. Zhang, and D. Li. Neurocomputing, (2019)

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On thoughtfulness and generosity in sequential decisions.F. Tang, and Y. Xu. Social Choice and Welfare, 37 (4): 707-715 (2011)Online pricing dynamics in Internet retailing: The case of the DVD market.B. Li, and F. Tang. Electronic Commerce Research and Applications, 10 (2): 227-236 (2011)Pricing Patterns in the Online CD Market: An Empirical Study.F. Tang, and D. Lu. Electron. Mark., 11 (3): 171-185 (2001)In Memoriam: John C. Harsanyi: Memory from China.F. Tang. Games and Economic Behavior, 40 (1): 150-152 (2002)Using insurance to create trust on the Internet.F. Tang, M. Thom, L. Wang, J. Tan, W. Chow, and X. Tang. Commun. ACM, 46 (12): 337-344 (2003)Adaptive Learning versus Punishment in Ultimatum Bargaining.K. Abbink, G. Bolton, A. Sadrieh, and F. Tang. Games and Economic Behavior, 37 (1): 1-25 (2001)Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation.L. Wang, F. Tang, and H. Wu. Applied Mathematics and Computation, 171 (2): 1141-1156 (2005)A 79dB THD CMOS low power THA circuit using pseudo differential source follower and common-mode management.W. Zhang, and F. Tang. ICECS, page 343-346. IEEE, (2014)Tightly-coupled navigation assistance in heterogeneous multi-robot teams.L. Parker, B. Kannan, F. Tang, and M. Bailey. IROS, page 1016-1022. IEEE, (2004)A deep manifold learning approach for spatial-spectral classification with limited labeled training samples.X. Zhou, N. Liu, F. Tang, Y. Zhao, K. Qin, L. Zhang, and D. Li. Neurocomputing, (2019)

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A deep manifold learning approach for spatial-spectral classification with limited labeled training samples.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A deep manifold learning approach for spatial-spectral classification with limited labeled training samples.

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A deep manifold learning approach for spatial-spectral classification with limited labeled training samples.