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» Online Multiple Instance Learning with No Regret
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STOC
1997
ACM
97views Algorithms» more  STOC 1997»
13 years 10 months ago
Using and Combining Predictors That Specialize
Abstract. We study online learning algorithms that predict by combining the predictions of several subordinate prediction algorithms, sometimes called “experts.” These simple a...
Yoav Freund, Robert E. Schapire, Yoram Singer, Man...
CRV
2006
IEEE
155views Robotics» more  CRV 2006»
13 years 12 months ago
Collaborative Multi-Camera Surveillance with Automated Person Detection
This paper presents the groundwork for a distributed network of collaborating, intelligent surveillance cameras, implemented with low-cost embedded microprocessor camera modules. ...
Trevor Ahmedali, James J. Clark
KDD
2009
ACM
227views Data Mining» more  KDD 2009»
14 years 6 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
IJCV
2008
106views more  IJCV 2008»
13 years 5 months ago
Evaluation of Localized Semantics: Data, Methodology, and Experiments
We present a new data set encoding localized semantics for 1014 images and a methodology for using this kind of data for recognition evaluation. This methodology establishes protoc...
Kobus Barnard, Quanfu Fan, Ranjini Swaminathan, An...
ATAL
2009
Springer
14 years 11 days ago
Stable multi-project scheduling of airport ground handling services by heterogeneous agents
This paper addresses decentralized multi-project scheduling under uncertainty. The problem instance we study is the scheduling of airport ground handling services, where aircraft ...
Xiaoyu Mao, Nico Roos, Alfons H. Salden