References of "Neural Processing Letters"
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See detailGenerative Modeling of Autonomous Robots and their Environments using Reservoir Computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Campenhout, Jan Van

in Neural Processing Letters (2007), 26(3), 233--249

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See detailA greedy EM algorithm for Gaussian mixture learning
Vlassis, Nikos UL; Likas, A.

in Neural Processing Letters (2002), 15(1), 77-87

Learning a Gaussian mixture with a local algorithm like EM can be difficult because (i) the true number of mixing components is usually unknown, (ii) there is no generally accepted method for parameter ... [more ▼]

Learning a Gaussian mixture with a local algorithm like EM can be difficult because (i) the true number of mixing components is usually unknown, (ii) there is no generally accepted method for parameter initialization, and (iii) the algorithm can get trapped in one of the many local maxima of the likelihood function. In this paper we propose a greedy algorithm for learning a Gaussian mixture which tries to overcome these limitations. In particular, starting with a single component and adding components sequentially until a maximum number k, the algorithm is capable of achieving solutions superior to EM with k components in terms of the likelihood of a test set. The algorithm is based on recent theoretical results on incremental mixture density estimation, and uses a combination of global and local search each time a new component is added to the mixture. [less ▲]

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See detailMixture density estimation based on Maximum Likelihood and test statistics
Vlassis, Nikos UL; Papakonstantinou, G.; Tsanakas, P.

in Neural Processing Letters (1999), 9(1), 63-76

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