Non-parametric e-mixture of Density Functions
暂无分享,去创建一个
Hideitsu Hino | Noboru Murata | Ken Takano | Shotaro Akaho | S. Akaho | H. Hino | Noboru Murata | Ken Takano
[1] Bregman divergence and density integration , 2009 .
[2] Shiliang Sun,et al. A subject transfer framework for EEG classification , 2012, Neurocomputing.
[3] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[4] Shrikanth Narayanan,et al. Information divergence estimation based on data-dependent partitions , 2010 .
[5] Qing Wang,et al. Divergence Estimation for Multidimensional Densities Via $k$-Nearest-Neighbor Distances , 2009, IEEE Transactions on Information Theory.
[6] Qing Wang,et al. Divergence estimation of continuous distributions based on data-dependent partitions , 2005, IEEE Transactions on Information Theory.
[7] R. A. Leibler,et al. On Information and Sufficiency , 1951 .
[8] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[9] Hideitsu Hino,et al. Information estimators for weighted observations , 2013, Neural Networks.
[10] Christian Genest,et al. Combining Probability Distributions: A Critique and an Annotated Bibliography , 1986 .
[11] Shun-ichi Amari,et al. Methods of information geometry , 2000 .
[12] Yasuji Sawada,et al. Neural network formation in an aggregate of dissociate hydra cells , 1988, Neural Networks.