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» Lightly supervised and unsupervised acoustic model training
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BMCBI
2006
119views more  BMCBI 2006»
13 years 7 months ago
Hidden Markov Model Variants and their Application
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
Stephen Winters-Hilt
NC
2002
196views Neural Networks» more  NC 2002»
13 years 7 months ago
Beyond second-order statistics for learning: A pairwise interaction model for entropy estimation
Second order statistics have formed the basis of learning and adaptation due to its appeal and analytical simplicity. On the other hand, in many realistic engineering problems requ...
Deniz Erdogmus, José Carlos Príncipe...
INTERSPEECH
2010
13 years 2 months ago
Deep-structured hidden conditional random fields for phonetic recognition
We extend our earlier work on deep-structured conditional random field (DCRF) and develop deep-structured hidden conditional random field (DHCRF). We investigate the use of this n...
Dong Yu, Li Deng
JMLR
2010
192views more  JMLR 2010»
13 years 2 months ago
Inducing Tree-Substitution Grammars
Inducing a grammar from text has proven to be a notoriously challenging learning task despite decades of research. The primary reason for its difficulty is that in order to induce...
Trevor Cohn, Phil Blunsom, Sharon Goldwater
JCP
2006
118views more  JCP 2006»
13 years 7 months ago
Learning a Classification-based Glioma Growth Model Using MRI Data
Gliomas are malignant brain tumors that grow by invading adjacent tissue. We propose and evaluate a 3D classification-based growth model, CDM, that predicts how a glioma will grow ...
Marianne Morris, Russell Greiner, Jörg Sander...