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» Pruning Training Sets for Learning of Object Categories
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NIPS
2008
13 years 10 months ago
Unsupervised Learning of Visual Sense Models for Polysemous Words
Polysemy is a problem for methods that exploit image search engines to build object category models. Existing unsupervised approaches do not take word sense into consideration. We...
Kate Saenko, Trevor Darrell
KCAP
2009
ACM
14 years 3 months ago
Reducing class imbalance during active learning for named entity annotation
In lots of natural language processing tasks, the classes to be dealt with often occur heavily imbalanced in the underlying data set and classifiers trained on such skewed data t...
Katrin Tomanek, Udo Hahn
ISF
2010
164views more  ISF 2010»
13 years 6 months ago
An SVM-based machine learning method for accurate internet traffic classification
Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven t...
Ruixi Yuan, Zhu Li, Xiaohong Guan, Li Xu
AVSS
2005
IEEE
14 years 2 months ago
Shape recognition based on a video and multi-sensor system
We present in this paper a real-time system for shape recognition. The proposed system is a video and multisensor platform that is able to classify the mobile objects evolving in ...
Huy-Binh Bui Ngoc, François Brémond,...
ACL
2001
13 years 10 months ago
Japanese Named Entity Recognition based on a Simple Rule Generator and Decision Tree Learning
Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detected and classified into categories such as person, organization, loc...
Hideki Isozaki