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» Machine Learning for Information Extraction
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KBS
2007
86views more  KBS 2007»
15 years 3 months ago
Automatic species identification of live moths
A collection consisting of the images of 774 live moth individuals, each moth belonging to one of 35 different UK species, was analysed to determine if data mining techniques could...
Michael Mayo, Anna T. Watson
ICIP
2009
IEEE
16 years 4 months ago
Learning Contextual Rules For Priming Object Categories In Images
In this paper we introduce and exploit the concept of contextual rules in the field of object detection. These rules are defined as associations between different object likelihoo...
KDD
2002
ACM
138views Data Mining» more  KDD 2002»
16 years 4 months ago
Learning to match and cluster large high-dimensional data sets for data integration
Part of the process of data integration is determining which sets of identifiers refer to the same real-world entities. In integrating databases found on the Web or obtained by us...
William W. Cohen, Jacob Richman
123
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ICASSP
2008
IEEE
15 years 10 months ago
A top-down auditory attention model for learning task dependent influences on prominence detection in speech
A top-down task-dependent model guides attention to likely target locations in cluttered scenes. Here, a novel biologically plausible top-down auditory attention model is presente...
Ozlem Kalinli, Shrikanth S. Narayanan
137
Voted
IJCNLP
2005
Springer
15 years 9 months ago
Heuristic Methods for Reducing Errors of Geographic Named Entities Learned by Bootstrapping
Abstract. One of issues in the bootstrapping for named entity recognition is how to control annotation errors introduced at every iteration. In this paper, we present several heuri...
Seungwoo Lee, Gary Geunbae Lee