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» Acceleration of the EM algorithm
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121
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NAACL
2010
15 years 1 months ago
Painless Unsupervised Learning with Features
We show how features can easily be added to standard generative models for unsupervised learning, without requiring complex new training methods. In particular, each component mul...
Taylor Berg-Kirkpatrick, Alexandre Bouchard-C&ocir...
121
Voted
ICDAR
2009
IEEE
15 years 1 months ago
Stochastic Model of Stroke Order Variation
A stochastic model of stroke order variation is proposed and applied to the stroke-order free on-line Kanji character recognition. The proposed model is a hidden Markov model (HMM...
Yoshinori Katayama, Seiichi Uchida, Hiroaki Sakoe
157
Voted
ICMLA
2009
15 years 1 months ago
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally qua...
Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapu...
96
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COLING
2010
14 years 10 months ago
EMDC: A Semi-supervised Approach for Word Alignment
This paper proposes a novel semisupervised word alignment technique called EMDC that integrates discriminative and generative methods. A discriminative aligner is used to find hig...
Qin Gao, Francisco Guzmán, Stephan Vogel
COLING
2010
14 years 10 months ago
Two Methods for Extending Hierarchical Rules from the Bilingual Chart Parsing
This paper studies two methods for training hierarchical MT rules independently of word alignments. Bilingual chart parsing and EM algorithm are used to train bitext correspondenc...
Martin Cmejrek, Bowen Zhou