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The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution.
Maximum likelihood from incomplete data via the EM algorithm
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Hierarchical mixtures of experts and the EM algorithm
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Is learning the n-th thing any easier than learning the first
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Inducing features of random fields
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Using out-of-domain data to improve in-domain language models
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Maximum conditional likelihood via bound maximization and the CEM algorithm
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Variational Methods in Optimization
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A Gaussian prior for smoothing maximum entropy models
Chen, S., and Rosenfeld, R. (1999) · 1999
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Supervised grammar induction using training data with limited constituent information
Hwa, R. (1999) · 1999
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On-line algorithms for combining language models
Kalai, A., Chen, S., Blum, A., and Rosenfeld, R. (1999) · 1999
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A model of inductive bias learning
Baxter, J. (2000) · 2000
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Maximum entropy Markov models for information extraction and segmentation
McCallum, A., Freitag, D., and Pereira, F. (2000) · 2000
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Corpus variation and parser performance
Gildea, D. (2001) · 2001
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Unsupervised langauge model adaptation
Bacchiani, M., and Roark, B. (2003) · 2003
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An extensive empirical study of feature selection metrics for text classification
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Nonparametric Statistical Inference
Gibbons, J. D., and Chakraborti, S. (2003) · 2003
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A comparison of numerical optimizers for logistic regression
Minka, T. P. (2003) · 2003
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Supervised and unsupervised PCFG adaptation to novel domains
Roark, B., and Bacchiani, M. (2003) · 2003
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Adaptation of maximum entropy classifier: Little data can help a lot
Chelba, C., and Acero, A. (2004) · 2004
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