We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a...
Hierarchical models have been extensively studied in various domains. However, existing models assume fixed model structures or incorporate structural uncertainty generatively. In...
Model-based clustering exploits finite mixture models for detecting group in a data set. It provides a sound statistical framework which can address some important issues, such as...
Gaussian Mixture Model (GMM) is one of the most popular data clustering methods which can be viewed as a linear combination of different Gaussian components. In GMM, each cluster ...
Blind separation of sources from nonlinear mixtures is a challenging and often ill-posed problem. We present three methods for solving this problem: an improved nonlinear factor a...
Antti Honkela, Harri Valpola, Alexander Ilin, Juha...