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» Generative Supervised Classification Using Dirichlet Process...
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ICML
2007
IEEE
14 years 6 months ago
The matrix stick-breaking process for flexible multi-task learning
In multi-task learning our goal is to design regression or classification models for each of the tasks and appropriately share information between tasks. A Dirichlet process (DP) ...
Ya Xue, David B. Dunson, Lawrence Carin
ICML
2007
IEEE
14 years 6 months ago
Discriminative Gaussian process latent variable model for classification
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional ...
Raquel Urtasun, Trevor Darrell
SIGIR
2004
ACM
13 years 11 months ago
A nonparametric hierarchical bayesian framework for information filtering
Information filtering has made considerable progress in recent years.The predominant approaches are content-based methods and collaborative methods. Researchers have largely conc...
Kai Yu, Volker Tresp, Shipeng Yu
NIPS
2003
13 years 7 months ago
Semi-Supervised Learning with Trees
We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure ...
Charles Kemp, Thomas L. Griffiths, Sean Stromsten,...
CIKM
2005
Springer
13 years 11 months ago
Generating better concept hierarchies using automatic document classification
This paper presents a hybrid concept hierarchy development technique for web returned documents retrieved by a meta-search engine. The aim of the technique is to separate the init...
Razvan Stefan Bot, Yi-fang Brook Wu, Xin Chen, Qua...