Analysis and design of decision-directed learning schemes using stochastic approximation
Information Sciences: an International Journal
Positive vectors clustering using inverted Dirichlet finite mixture models
Expert Systems with Applications: An International Journal
Hi-index | 754.84 |
A stochastic approximation algorithm is developed for estimating a mixture of normal density functions with unknown means and unknown variances. The algorithm minimizes an information criterion that has interesting properties for density approximations. The conditions on the convergence of this nonlinear estimation algorithm are discussed, and a numerical example is presented.