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» Learning Gaussian Process Models from Uncertain Data
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ACL
2010
13 years 6 months ago
Hierarchical Joint Learning: Improving Joint Parsing and Named Entity Recognition with Non-Jointly Labeled Data
One of the main obstacles to producing high quality joint models is the lack of jointly annotated data. Joint modeling of multiple natural language processing tasks outperforms si...
Jenny Rose Finkel, Christopher D. Manning
BMCBI
2006
118views more  BMCBI 2006»
13 years 8 months ago
Predicting the effect of missense mutations on protein function: analysis with Bayesian networks
Background: A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, ...
Chris J. Needham, James R. Bradford, Andrew J. Bul...
WCRE
2003
IEEE
14 years 1 months ago
Fuzzy Extensions for Reverse Engineering Repository Models
Reverse Engineering is a process fraught with imperfections. The importance of dealing with non-precise, possibly inconsistent data explicitly when interacting with the reverse en...
Ulrike Kölsch, René Witte
TSD
2007
Springer
14 years 2 months ago
Maximum Likelihood and Maximum Mutual Information Training in Gender and Age Recognition System
Abstract. Gender and age estimation based on Gaussian Mixture Models (GMM) is introduced. Telephone recordings from the Czech SpeechDatEast database are used as training and test d...
Valiantsina Hubeika, Igor Szöke, Lukas Burget...
IJCNN
2008
IEEE
14 years 2 months ago
On the learning of nonlinear visual features from natural images by optimizing response energies
— The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortic...
Jussi T. Lindgren, Aapo Hyvärinen