There are three kinds of probabilistic graph, namely (1) directed graph(Bayesian network); (2) undirected graph(Markov network); (3) hybrid graph(Chain graph). We can represent the joint distribution over a Bayesian network by chain rule, and represent a joint distribution over a Markov network a ...
In Daphne Koller's book (probabilistic graphical model: principles and techniques), there are 3 kinds of Markov Properties: pairwise Markov Property, Local Markov Property and Global Markov Property. Obviously, global indicates local, and local indicates pairwise. However, the reverse is not necessa ...
CRF and MRF are two popular graphical models, which is an intuitive model to model all kinds of networks. Recently deep belief network has aroused a huge surge in Machine Learning community, and it got state of the art results in numerous fields, like object recognition, acoustic understanding and t ...
A research group from Canada has built a system to execute large-scale neural networks, which simulates some functions of human brains. Project homepage: http://nengo.ca/ publication on Science: A large - scale model of the functioning brain
(1) LLE (Prof.Roweis, Unfortunately, he passes away): Nonlinear dimensionality reduction by locally linear embedding (2) ISOMAP: A global geometric framework for nonlinear dimensionality reduction (3) eigenmap: Laplacian eigenmaps for dimensionality reduction and data re ...
(1) Daphne Koller (@stanford): Probabilistic graphical models : principles and techniques cofounder of coursera (with Andrew Ng@stanford ): http://www.ted.com/talks/daphne_koller_what_we_re_learning_from_online_education.html (2) Kevin Patrick Murphy(@google, previously @ubc): Mac ...
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