Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information
暂无分享,去创建一个
[1] Shun-ichi Amari,et al. A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..
[2] M. Kendall,et al. Kendall's advanced theory of statistics , 1995 .
[3] Peter Földiák,et al. Adaptation and decorrelation in the cortex , 1989 .
[4] Richard Durbin,et al. The computing neuron , 1989 .
[5] Shun-ichi Amari,et al. Backpropagation and stochastic gradient descent method , 1993, Neurocomputing.
[6] J. Nadal,et al. Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer Network 5 , 1994 .
[7] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[8] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[9] Gustavo Deco,et al. Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures , 1995, Neural Networks.
[10] A. J. Bell,et al. Fast blind separation based on information theory , 1995 .
[11] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[12] Jean-François Cardoso,et al. Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..
[13] Shun-ichi Amari,et al. Neural Learning in Structured Parameter Spaces - Natural Riemannian Gradient , 1996, NIPS.
[14] Andrew D. Back,et al. A First Application of Independent Component Analysis to Extracting Structure from Stock Returns , 1997, Int. J. Neural Syst..
[15] Petteri Pajunen,et al. Blind source separation using algorithmic information theory , 1998, Neurocomputing.
[16] Jean-Francois Cardoso,et al. Blind signal separation: statistical principles , 1998, Proc. IEEE.
[17] Norimichi Tsumura,et al. Independent Component Analysis of Skin Color Image , 1998, CIC.
[18] Shun-ichi Amari,et al. Adaptive blind signal processing-neural network approaches , 1998, Proc. IEEE.
[19] S. Amari,et al. Statistical inference: learning in artificial neural networks , 1998, Trends in Cognitive Sciences.
[20] Erkki Oja,et al. The nonlinear PCA criterion in blind source separation: Relations with other approaches , 1998, Neurocomputing.
[21] Shun-ichi Amari,et al. Learned parametric mixture based ICA algorithm , 1998, Neurocomputing.
[22] Andrzej Cichocki,et al. Information-theoretic approach to blind separation of sources in non-linear mixture , 1998, Signal Process..
[23] Juha Karhunen,et al. Neural networks for blind separation with unknown number of sources , 1999, Neurocomputing.