A variational level set method is developed for the supervised classification problem. Nonlinear classifier decision boundaries are obtained by minimizing an energy functional tha...
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the...
David Baehrens, Timon Schroeter, Stefan Harmeling,...
Contrastive Divergence (CD) is a popular method for estimating the parameters of Markov Random Fields (MRFs) by rapidly approximating an intractable term in the gradient of the lo...
We introduce a new method -- the group Dantzig selector -- for high dimensional sparse regression with group structure, which has a convincing theory about why utilizing the group...
A large body of prior research on coreference resolution recasts the problem as a two-class classification problem. However, standard supervised machine learning algorithms that m...