Van Hulle MM. (2005). Maximum likelihood topographic map formation. Neural computation. 17 [PubMed]

See more from authors: Van Hulle MM

References and models cited by this paper

Ahmad IA, Lin PE. (1976). A nonparametric estimation of the entropy for absolutely continuous distributions IEEE Trans Information Theory. 22

AndrĂ¡s P. (2002). Kernel-Kohonen networks. International journal of neural systems. 12 [PubMed]

Benaim M, Tomasini L. (1991). Competitive and self-organizing algorithms based on the minimization of an information criterion Proc ICANN91.

Dempster AP, Laird NM, Rubin DB. (1977). Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 39

Gyorfi L, Beirlant J, Dudewicz EJ, van_der_Meulen EC. (1997). Nonparametric entropy estimation: An overview Int J Math And Statistical Sciences. 6

Heskes T. (2001). Self-organizing maps, vector quantization, and mixture modeling. IEEE transactions on neural networks. 12 [PubMed]

Heskes TM, Kappen B. (1993). Error potentials for self-organization Proc. IEEE Int. Conf. on Neural Networks.

Kohonen T. (1995). Self-organizing Maps.

Linsker R. (1989). How to generate maps by maximizing the mutual information between input and output signals Neural Comput. 1

Luttrell SP. (1991). Code vector density in topographic mappings: Scalar case. IEEE transactions on neural networks. 2 [PubMed]

Obermayer K, Graepel T, Burger M. (1997). Phase transitions in stochastic self-organizing maps Physical Rev E. 56

Obermayer K, Graepel T, Burger M. (1998). Self-organizing maps: Generalizations and new optimization techniques Neurocomputing. 21

Redner RA, Walker HF. (1984). Mixture densities, maximum likelihood and the EM algorithm SIAM Rev. 26

Rice JA. (1995). Mathematical statistics and data analysis.

Van Hulle MM. (1998). Kernel-Based Equiprobabilistic Topographic Map Formation. Neural computation. 10 [PubMed]

Van Hulle MM. (2002). Joint entropy maximization in kernel-based topographic maps. Neural computation. 14 [PubMed]

Vapnik V. (1995). The Nature of Statistical Learning Theory.

Yin H, Allinson NM. (2001). Self-organizing mixture networks for probability density estimation. IEEE transactions on neural networks. 12 [PubMed]

References and models that cite this paper

Van Hulle MM. (2005). Differential Log Likelihood for Evaluating and Learning Gaussian Mixtures Neural Comput. 18

This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.