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» Gene function prediction using labeled and unlabeled data
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SDM
2008
SIAM
139views Data Mining» more  SDM 2008»
15 years 1 months ago
Semi-Supervised Learning Based on Semiparametric Regularization
Semi-supervised learning plays an important role in the recent literature on machine learning and data mining and the developed semisupervised learning techniques have led to many...
Zhen Guo, Zhongfei (Mark) Zhang, Eric P. Xing, Chr...
CIKM
2008
Springer
15 years 1 months ago
Intra-document structural frequency features for semi-supervised domain adaptation
In this work we try to bridge the gap often encountered by researchers who find themselves with few or no labeled examples from their desired target domain, yet still have access ...
Andrew Arnold, William W. Cohen
ICML
2010
IEEE
15 years 27 days ago
Label Ranking Methods based on the Plackett-Luce Model
This paper introduces two new methods for label ranking based on a probabilistic model of ranking data, called the Plackett-Luce model. The idea of the first method is to use the ...
Weiwei Cheng, Krzysztof Dembczynski, Eyke Hül...
PRL
2007
98views more  PRL 2007»
14 years 11 months ago
Error probabilities for local extrema in gene expression data
Current approaches for the prediction of functional relations from gene expression data often do not have a clear methodology for extracting features and are not accompanied by a ...
Perry Groot, Christian Gilissen, Michael Egmont-Pe...
ICML
2008
IEEE
16 years 19 days ago
A unified architecture for natural language processing: deep neural networks with multitask learning
We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity...
Ronan Collobert, Jason Weston