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» Learning aspect models with partially labeled data
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ML
2000
ACM
124views Machine Learning» more  ML 2000»
15 years 3 months ago
Text Classification from Labeled and Unlabeled Documents using EM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
ICASSP
2011
IEEE
14 years 7 months ago
A partial least squares framework for speaker recognition
Modern approaches to speaker recognition (verification) operate in a space of “supervectors” created via concatenation of the mean vectors of a Gaussian mixture model (GMM) a...
Balaji Vasan Srinivasan, Dmitry N. Zotkin, Ramani ...
NIPS
2008
15 years 5 months ago
Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree
Unexpected stimuli are a challenge to any machine learning algorithm. Here we identify distinct types of unexpected events, focusing on 'incongruent events' when 'g...
Daphna Weinshall, Hynek Hermansky, Alon Zweig, Jie...
NLE
2008
140views more  NLE 2008»
15 years 3 months ago
Active learning and logarithmic opinion pools for HPSG parse selection
For complex tasks such as parse selection, the creation of labelled training sets can be extremely costly. Resource-efficient schemes for creating informative labelled material mu...
Jason Baldridge, Miles Osborne
128
Voted
ICML
2008
IEEE
16 years 4 months ago
Semi-supervised learning of compact document representations with deep networks
Finding good representations of text documents is crucial in information retrieval and classification systems. Today the most popular document representation is based on a vector ...
Marc'Aurelio Ranzato, Martin Szummer