T ORDER REGRESSION (EXTENDED ABSTRACT) Kurt Driessensa Saso Dzeroskib a Department of Computer Science, University of Waikato, Hamilton, New Zealand (kurtd@waikato.ac.nz) b Departm...
evel of abstraction, we can represent a workflow as a directed graph with operators (or tasks) at the vertices (see Figure 1). Each operator takes inputs from data sources or from ...
Despite popular belief, boosting algorithms and related coordinate descent methods are prone to overfitting. We derive modifications to AdaBoost and related gradient-based coordin...
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning ML...
Nonlinear ICA may not result in nonlinear blind source separation, since solutions to nonlinear ICA are highly non-unique. In practice, the nonlinearity in the data generation pro...