Open AccessProceedings Article
Latent variable perceptron algorithm for structured classification
Xu Sun,Takuya Matsuzaki,Daisuke Okanohara,Jun'ichi Tsujii +3 more
- 11 Jul 2009
- pp 1236-1242
TL;DR: Compared to existing probabilistic models of latent variables, the proposed perceptron-style algorithm lowers the training cost significantly yet with comparable or even superior classification accuracy.
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Abstract: We propose a perceptron-style algorithm for fast discriminative training of structured latent variable model, and analyzed its convergence properties. Our method extends the perceptron algorithm for the learning task with latent dependencies, which may not be captured by traditional models. It relies on Viterbi decoding over latent variables, combined with simple additive updates. Compared to existing probabilistic models of latent variables, our method lowers the training cost significantly yet with comparable or even superior classification accuracy.
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References
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
The perceptron: a probabilistic model for information storage and organization in the brain.
TL;DR: This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory.
10.6K
•Book
The perception: a probabilistic model for information storage and organization in the brain
F. Rosenblatt
- 01 Jan 1988
TL;DR: The second and third questions are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory as mentioned in this paper.
9.3K
Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms
Michael Collins
- 06 Jul 2002
TL;DR: Experimental results on part-of-speech tagging and base noun phrase chunking are given, in both cases showing improvements over results for a maximum-entropy tagger.
Shallow parsing with conditional random fields
Fei Sha,Fernando Pereira +1 more
- 27 May 2003
TL;DR: This work shows how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model.
1.5K