论文信息 - Fuzzy c-Means Algorithms Using Kullback-Leibler Divergence and Helliger Distance Based on Multinomial Manifold - 字舞流文

Fuzzy c-Means Algorithms Using Kullback-Leibler Divergence and Helliger Distance Based on Multinomial Manifold

Sadaaki Miyamoto | Ryo Inokuchi

[1] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .

[2] R. Kass,et al. Geometrical Foundations of Asymptotic Inference: Kass/Geometrical , 1997 .

[3] Hinrich Schütze,et al. Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[4] John D. Lafferty,et al. Diffusion Kernels on Statistical Manifolds , 2005, J. Mach. Learn. Res..

[5] Sadaaki Miyamoto,et al. Fuzzy c-means as a regularization and maximum entropy approach , 1997 .

[6] Sanjoy Dasgupta,et al. A Generalization of Principal Components Analysis to the Exponential Family , 2001, NIPS.

[7] James C. Bezdek,et al. Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[8] Tony Jebara,et al. Probability Product Kernels , 2004, J. Mach. Learn. Res..