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NIPS
1998
13 years 6 months ago
Semi-Supervised Support Vector Machines
We introduce a semi-supervised support vector machine (S3 VM) method. Given a training set of labeled data and a working set of unlabeled data, S3 VM constructs a support vector m...
Kristin P. Bennett, Ayhan Demiriz
JASIS
2000
143views more  JASIS 2000»
13 years 5 months ago
Discovering knowledge from noisy databases using genetic programming
s In data mining, we emphasize the need for learning from huge, incomplete and imperfect data sets (Fayyad et al. 1996, Frawley et al. 1991, Piatetsky-Shapiro and Frawley, 1991). T...
Man Leung Wong, Kwong-Sak Leung, Jack C. Y. Cheng
ACL
2010
13 years 3 months ago
Open Information Extraction Using Wikipedia
Information-extraction (IE) systems seek to distill semantic relations from naturallanguage text, but most systems use supervised learning of relation-specific examples and are th...
Fei Wu 0003, Daniel S. Weld
EMNLP
2009
13 years 3 months ago
Classifying Relations for Biomedical Named Entity Disambiguation
Named entity disambiguation concerns linking a potentially ambiguous mention of named entity in text to an unambiguous identifier in a standard database. One approach to this task...
Xinglong Wang, Jun-ichi Tsujii, Sophia Ananiadou
IJCSS
2006
116views more  IJCSS 2006»
13 years 5 months ago
Extracting Motor Unit Firing Information by Independent Component Analysis of Surface Electromyogram: A Preliminary Study Using
Decomposition of electromyogram (EMG) provides a valuable means of obtaining motor unit recruitment and firing rate information. The feasibility of decomposing surface EMG signals...
Ping Zhou, M. M. Lowery, W. Zev Rymer