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Feature detectors by autoencoders: Decomposition of input patterns into atomic features by neural networks.

R. Kamimura, and S. Nakanishi. Neural Processing Letters, 2 (6): 17-22 (1995)

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Controlling α-entropy with a Neural α-Feature Detector.R. Kamimura. Complex Syst., (1997)Repeated comprehensibility maximization in competitive learning.R. Kamimura. Neural Comput. Appl., 22 (5): 911-932 (2013)Forced information and information loss in information-theoretic competitive learning.R. Kamimura. Artificial Intelligence and Applications, page 78-84. IASTED/ACTA Press, (2007)Constrained information maximization by free energy minimization.R. Kamimura. Int. J. General Systems, 40 (7): 701-725 (2011)Similarity interaction in information-theoretic self-organizing maps.R. Kamimura. Int. J. General Systems, 42 (3): 239-267 (2013)Feature detectors by autoencoders: Decomposition of input patterns into atomic features by neural networks.R. Kamimura, and S. Nakanishi. Neural Processing Letters, 2 (6): 17-22 (1995)Contradiction Resolution for Foreign Exchange Rates Estimation.R. Kamimura. IJCCI, page 529-535. SciTePress, (2012)Pseudo-potentiality maximization for improved interpretation and generalization in neural networks.R. Kamimura. IWCIA, page 21-28. IEEE, (2015)Teacher-Directed Learning with Gaussian and Sigmoid Activation Functions.R. Kamimura. ICONIP, volume 3316 of Lecture Notes in Computer Science, page 530-536. Springer, (2004)Simplified Information Acquisition Method to Improve Prediction Performance: Direct Use of Hidden Neuron Outputs and Separation of Information Acquisition and Use Phase.R. Kamimura. ANNIIP, page 78-87. SciTePress, (2014)

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Feature detectors by autoencoders: Decomposition of input patterns into atomic features by neural networks.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Feature detectors by autoencoders: Decomposition of input patterns into atomic features by neural networks.

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Feature detectors by autoencoders: Decomposition of input patterns into atomic features by neural networks.