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KDD
2000
ACM
133views Data Mining» more  KDD 2000»
15 years 1 months ago
Data selection for support vector machine classifiers
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave minimiza...
Glenn Fung, Olvi L. Mangasarian
TREC
2008
14 years 11 months ago
UTDallas at TREC 2008 Blog Track
This paper describes our participation in the 2008 TREC Blog track. Our system consists of 3 components: data preprocessing, topic retrieval, and opinion finding. In the topic ret...
Bin Li, Feifan Liu, Yang Liu
JSW
2008
106views more  JSW 2008»
14 years 9 months ago
Hierarchical Image Segmentation by Structural Content
Image quality loss resulting from artifacts depends on the nature and strength of the artifacts as well as the context or background in which they occur. In order to include the im...
Nathir A. Rawashdeh, Shaun T. Love, Kevin D. Donoh...
UMUAI
2008
192views more  UMUAI 2008»
14 years 9 months ago
Automatic detection of learner's affect from conversational cues
We explored the reliability of detecting a learner's affect from conversational features extracted from interactions with AutoTutor, an intelligent tutoring system that helps...
Sidney K. D'Mello, Scotty D. Craig, Amy M. Withers...
PRL
2008
213views more  PRL 2008»
14 years 9 months ago
Boosting recombined weak classifiers
Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the comb...
Juan José Rodríguez, Jesús Ma...