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» The Inefficiency of Batch Training for Large Training Sets
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EMNLP
2004
13 years 10 months ago
Trained Named Entity Recognition using Distributional Clusters
This work applies boosted wrapper induction (BWI), a machine learning algorithm for information extraction from semi-structured documents, to the problem of named entity recogniti...
Dayne Freitag
NAACL
2010
13 years 6 months ago
Integrating Joint n-gram Features into a Discriminative Training Framework
Phonetic string transduction problems, such as letter-to-phoneme conversion and name transliteration, have recently received much attention in the NLP community. In the past few y...
Sittichai Jiampojamarn, Colin Cherry, Grzegorz Kon...
ICML
2009
IEEE
14 years 9 months ago
Proximal regularization for online and batch learning
Many learning algorithms rely on the curvature (in particular, strong convexity) of regularized objective functions to provide good theoretical performance guarantees. In practice...
Chuong B. Do, Quoc V. Le, Chuan-Sheng Foo
IJON
2008
173views more  IJON 2008»
13 years 8 months ago
Support vector machine classification for large data sets via minimum enclosing ball clustering
Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitabl...
Jair Cervantes, Xiaoou Li, Wen Yu, Kang Li
ICML
2004
IEEE
14 years 9 months ago
Improving SVM accuracy by training on auxiliary data sources
The standard model of supervised learning assumes that training and test data are drawn from the same underlying distribution. This paper explores an application in which a second...
Pengcheng Wu, Thomas G. Dietterich