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» Learning from Multiple Annotators with Gaussian Processes
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ICML
2007
IEEE
14 years 6 months ago
Multifactor Gaussian process models for style-content separation
We introduce models for density estimation with multiple, hidden, continuous factors. In particular, we propose a generalization of multilinear models using nonlinear basis functi...
Jack M. Wang, David J. Fleet, Aaron Hertzmann
ICONIP
2009
13 years 3 months ago
Learning Gaussian Process Models from Uncertain Data
It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
Patrick Dallaire, Camille Besse, Brahim Chaib-draa
ICPR
2010
IEEE
13 years 9 months ago
Gaussian Process Learning from Order Relationships Using Expectation Propagation
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation propagation is used to obtain an approximation to...
Ruixuan Wang, Stephen James Mckenna
ICDM
2010
IEEE
228views Data Mining» more  ICDM 2010»
13 years 2 months ago
Active Learning from Multiple Noisy Labelers with Varied Costs
In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, ...
Yaling Zheng, Stephen D. Scott, Kun Deng
ICWSM
2009
13 years 3 months ago
Regression-Based Summarization of Email Conversations
In this paper we present a regression-based machine learning approach to email thread summarization. The regression model is able to take advantage of multiple gold-standard annot...
Jan Ulrich, Giuseppe Carenini, Gabriel Murray, Ray...