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 compare the practical performance of several recently proposed algorithms for active learning in the online classification setting. We consider two active learning algorithms (...
We develop a novel online learning algorithm for the group lasso in order to efficiently find the important explanatory factors in a grouped manner. Different from traditional bat...
Haiqin Yang, Zenglin Xu, Irwin King, Michael R. Ly...
We design algorithms for two online variance minimization problems. Specifically, in every trial t our algorithms get a covariance matrix Ct and try to select a parameter vector wt...
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...