Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. In this paper, we consider the problem of learning shared s...
Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for l...
This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...
We present an ensemble learning approach that achieves accurate predictions from arbitrarily partitioned data. The partitions come from the distributed processing requirements of ...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
This paper presents an automatic algorithm which reconstructs building models from airborne LiDAR (light detection and ranging) data of urban areas. While our algorithm inherits t...