Graphical models, learning in high dimensions, tree networks.

University of Washington. Machine learning, probabilistic reasoning, graphical probability models, tree belief networks and mixtures of trees, maximum entropy discrimination, spectral clustering and image segmentation.

Model-based clustering, discriminant analysis, and density estimation including hierarchical clustering and EM for parameterized Gaussian mixtures + Poisson noise. Written in Fortran and interfaced to the S-PLUS commercial software package and the R l...

Model based clustering and discriminant analysis, including hierarchical clustering and EM. Developed at University of Washington.