The max-sum classifier predicts n-tuple of labels from n-tuple of observable variables by maximizing a sum of quality functions defined over neighbouring pairs of labels and obser...
The paper studies machine learning problems where each example is described using a set of Boolean features and where hypotheses are represented by linear threshold elements. One ...
We describe a novel family of PAC model algorithms for learning linear threshold functions. The new algorithms work by boosting a simple weak learner and exhibit complexity bounds...
Significant changes in the instance distribution or associated cost function of a learning problem require one to reoptimize a previously-learned classifier to work under new cond...
Chris Bourke, Kun Deng, Stephen D. Scott, Robert E...
Many of the recently proposed algorithms for learning feature-based ranking functions are based on the pairwise preference framework, in which instead of taking documents in isola...
Vitor R. Carvalho, Jonathan L. Elsas, William W. C...