关于互信息准则在分类(包括拒识类别)问题中的应用

[1]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[2]  Chong-Ho Choi,et al.  Input feature selection for classification problems , 2002, IEEE Trans. Neural Networks.

[3]  Tang Min,et al.  A Novel Image Registration Method Combining Morphological Gradient Mutual Information with Multiresolution Optimizer , 2009 .

[4]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[5]  R. F. Wagner,et al.  Assessment of medical imaging systems and computer aids: a tutorial review. , 2007, Academic radiology.

[6]  Min Tang.  A Novel Image Registration Method Combining Morphological Gradient Mutual Information with Multiresolution Optimizer: A Novel Image Registration Method Combining Morphological Gradient Mutual Information with Multiresolution Optimizer , 2009 .

[7]  Martin E. Hellman,et al.  Probability of error, equivocation, and the Chernoff bound , 1970, IEEE Trans. Inf. Theory.

[8]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  John W. Fisher,et al.  Learning from Examples with Information Theoretic Criteria , 2000, J. VLSI Signal Process..

[10]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[11]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[12]  D. V. Sridhar,et al.  Information theoretic subset selection for neural network models , 1998 .

[13]  Chungyong Lee,et al.  An information-theoretic perspective on feature selection in speaker recognition , 2005, IEEE Signal Processing Letters.

[14]  Ralph Linsker,et al.  Self-organization in a perceptual network , 1988, Computer.