Training algorithms for hidden conditional random fields

by: Milind Mahajan, Asela Gunawardana, Alex Acero

(2006)

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摘要

We investigate algorithms for training hidden conditional random fields (HCRFs) – a class of direct models with hidden state sequences. We compare stochastic gradient ascent with the RProp algorithm, and investigate stochastic versions of RProp. We propose a new scheme for model flattening, and compare it to the state of the art. Finally we give experimental results on the TIMIT phone classification task showing how these training options interact, comparing HCRFs to HMMs trained using extended Baum-Welch as well as stochastic gradient methods.

@inproceedings{citeulike:2795073, abstract = {We investigate algorithms for training hidden conditional random fields (HCRFs) – a class of direct models with hidden state sequences. We compare stochastic gradient ascent with the RProp algorithm, and investigate stochastic versions of RProp. We propose a new scheme for model flattening, and compare it to the state of the art. Finally we give experimental results on the TIMIT phone classification task showing how these training options interact, comparing HCRFs to HMMs trained using extended Baum-Welch as well as stochastic gradient methods.}, author = {Mahajan, Milind and Gunawardana, Asela and Acero, Alex }, booktitle = {International Conference on Acoustics, Speech, and Signal Processing}, citeulike-article-id = {2795073}, keywords = {crf, discriminative, hcrf, machine-learning}, posted-at = {2008-05-13 14:54:42}, priority = {2}, title = {Training algorithms for hidden conditional random fields}, url = {http://research.microsoft.com/~aselag/pubs/mahajan06\_\_train\_algor\_condit\_random\_field.pdf}, year = {2006} }

RIS record

TY - CONF ID - citeulike:2795073 L3 - citeulike-article-id:2795073 TI - Training algorithms for hidden conditional random fields T2 - International Conference on Acoustics, Speech, and Signal Processing N2 - We investigate algorithms for training hidden conditional random fields (HCRFs) – a class of direct models with hidden state sequences. We compare stochastic gradient ascent with the RProp algorithm, and investigate stochastic versions of RProp. We propose a new scheme for model flattening, and compare it to the state of the art. Finally we give experimental results on the TIMIT phone classification task showing how these training options interact, comparing HCRFs to HMMs trained using extended Baum-Welch as well as stochastic gradient methods. KW - crf KW - discriminative KW - hcrf KW - machine-learning AU - Mahajan, Milind AU - Gunawardana, Asela AU - Acero, Alex PY - 2006/// UR - http://research.microsoft.com/~aselag/pubs/mahajan06__train_algor_condit_random_field.pdf ER -

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