In multi-task learning our goal is to design regression or classification models for each of the tasks and appropriately share information between tasks. A Dirichlet process (DP) ...
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
We consider the problem of unsupervised learning from a matrix of data vectors where in each row the observed values are randomly permuted in an unknown fashion. Such problems ari...
The applicationofboosting procedures to decision tree algorithmshas been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a numbe...
Abstract. We consider the problem of efficient approximate learning by multilayered feedforward circuits subject to two objective functions. First, we consider the objective to ma...