Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection
暂无分享,去创建一个
[1] Stanley C. Ahalt,et al. Competitive learning algorithms for vector quantization , 1990, Neural Networks.
[2] Hamparsum Bozdogan,et al. Mixture-Model Cluster Analysis Using Model Selection Criteria and a New Informational Measure of Complexity , 1994 .
[3] P. J. Green,et al. Density Estimation for Statistics and Data Analysis , 1987 .
[4] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[5] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[6] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[7] A. Raftery,et al. Model-based Gaussian and non-Gaussian clustering , 1993 .
[8] D. N. Geary. Mixture Models: Inference and Applications to Clustering , 1989 .
[9] Yu-ming Cheung. Rival penalization controlled competitive learning for data clustering with unknown cluster number , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..
[10] Sang Uk Lee,et al. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques , 1990, Pattern Recognit..
[11] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[12] Gregory Piatetsky-Shapiro,et al. Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.
[13] R. Redner,et al. Mixture densities, maximum likelihood, and the EM algorithm , 1984 .
[14] Michael A. Arbib,et al. Color Image Segmentation using Competitive Learning , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[15] Erkki Oja,et al. Rival penalized competitive learning for clustering analysis, RBF net, and curve detection , 1993, IEEE Trans. Neural Networks.
[16] Lei Xu,et al. Rival penalized competitive learning, finite mixture, and multisets clustering , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[17] Bernd Fritzke,et al. The LBG-U Method for Vector Quantization – an Improvement over LBG Inspired from Neural Networks , 1997, Neural Processing Letters.
[18] H. Akaike. A new look at the statistical model identification , 1974 .
[19] Yiu-ming Cheung,et al. k*-Means: A new generalized k-means clustering algorithm , 2003, Pattern Recognit. Lett..
[20] H. Bozdogan. Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .
[21] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[22] Josef Kittler,et al. Pattern recognition : a statistical approach , 1982 .
[23] Lei Xu,et al. Bayesian Ying-Yang machine, clustering and number of clusters , 1997, Pattern Recognit. Lett..
[24] Robert M. Gray,et al. An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..