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63
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NAACL
2003
14 years 10 months ago
Active Learning for Classifying Phone Sequences from Unsupervised Phonotactic Models
This paper describes an application of active learning methods to the classification of phone strings recognized using unsupervised phonotactic models. The only training data req...
Shona Douglas
107
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JMLR
2010
116views more  JMLR 2010»
14 years 4 months ago
Feature Selection, Association Rules Network and Theory Building
As the size and dimensionality of data sets increase, the task of feature selection has become increasingly important. In this paper we demonstrate how association rules can be us...
Sanjay Chawla
CVPR
2012
IEEE
12 years 12 months ago
Geodesic flow kernel for unsupervised domain adaptation
In real-world applications of visual recognition, many factors—such as pose, illumination, or image quality—can cause a significant mismatch between the source domain on whic...
Boqing Gong, Yuan Shi, Fei Sha, Kristen Grauman
FGR
2008
IEEE
203views Biometrics» more  FGR 2008»
15 years 3 months ago
Spatiotemporal pyramid representation for recognition of facial expressions and hand gestures
This paper presents a spatiotemporal pyramid representation for recognizing facial expressions and hand gestures. This approach works by partitioning video sequence into increasin...
Zhipeng Zhao, Ahmed M. Elgammal
130
Voted
CVPR
2009
IEEE
16 years 4 months ago
Dense saliency-based spatiotemporal feature points for action recognition
Several spatiotemporal feature point detectors have been recently used in video analysis for action recognition. Feature points are detected using a number of measures, namely sali...
Konstantinos Rapantzikos, Stefanos D. Kollias, Yan...