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» Evaluating algorithms that learn from data streams
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ICML
2009
IEEE
16 years 5 months ago
A convex formulation for learning shared structures from multiple tasks
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...
Jianhui Chen, Lei Tang, Jun Liu, Jieping Ye
NIPS
2004
15 years 5 months ago
Instance-Specific Bayesian Model Averaging for Classification
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...
Shyam Visweswaran, Gregory F. Cooper
KDD
2005
ACM
177views Data Mining» more  KDD 2005»
16 years 4 months ago
Query chains: learning to rank from implicit feedback
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 ...
Filip Radlinski, Thorsten Joachims
ICTAI
2006
IEEE
15 years 10 months ago
Learning to Predict Salient Regions from Disjoint and Skewed Training Sets
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...
GIS
2008
ACM
16 years 5 months ago
Fast and extensible building modeling from airborne LiDAR data
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...
Qian-Yi Zhou, Ulrich Neumann