Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational ma...
Sample selection bias is a common problem in many real world applications, where training data are obtained under realistic constraints that make them follow a different distribut...
Having access to large data sets for the purpose of predictive data mining does not guarantee good models, even when the size of the training data is virtually unlimited. Instead,...
Testing remains a major challenge for model transformation development. Test models that are used as test data for model transformations, are constrained by various sources of kno...
We propose an omnivariate decision tree architecture which contains univariate, multivariate linear or nonlinear nodes, matching the complexity of the node to the complexity of the...