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» Evaluating algorithms that learn from data streams
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CVPR
2005
IEEE
16 years 8 months ago
Semi-Supervised Adapted HMMs for Unusual Event Detection
We address the problem of temporal unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the applica...
Dong Zhang, Daniel Gatica-Perez, Samy Bengio, Iain...
KDD
2007
ACM
190views Data Mining» more  KDD 2007»
16 years 7 months ago
Model-shared subspace boosting for multi-label classification
Typical approaches to multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computati...
Rong Yan, Jelena Tesic, John R. Smith
JAIR
2007
87views more  JAIR 2007»
15 years 6 months ago
Supporting Temporal Reasoning by Mapping Calendar Expressions to Minimal Periodic Sets
In the recent years several research efforts have focused on the concept of time granularity and its applications. A first stream of research investigated the mathematical model...
Claudio Bettini, Sergio Mascetti, Xiaoyang Sean Wa...
ICML
2003
IEEE
16 years 7 months ago
Learning Mixture Models with the Latent Maximum Entropy Principle
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from ...
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin...
ICGI
2010
Springer
15 years 7 months ago
Distributional Learning of Some Context-Free Languages with a Minimally Adequate Teacher
Angluin showed that the class of regular languages could be learned from a Minimally Adequate Teacher (mat) providing membership and equivalence queries. Clark and Eyraud (2007) sh...
Alexander Clark