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» Learning from Multiple Annotators with Gaussian Processes
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EMNLP
2008
13 years 8 months ago
An Analysis of Active Learning Strategies for Sequence Labeling Tasks
Active learning is well-suited to many problems in natural language processing, where unlabeled data may be abundant but annotation is slow and expensive. This paper aims to shed ...
Burr Settles, Mark Craven
EDBT
2009
ACM
113views Database» more  EDBT 2009»
14 years 2 months ago
Supporting annotations on relations
Annotations play a key role in understanding and curating databases. Annotations may represent comments, descriptions, lineage information, among several others. Annotation manage...
Mohamed Y. Eltabakh, Walid G. Aref, Ahmed K. Elmag...
ICDM
2008
IEEE
193views Data Mining» more  ICDM 2008»
14 years 1 months ago
Multiplicative Mixture Models for Overlapping Clustering
The problem of overlapping clustering, where a point is allowed to belong to multiple clusters, is becoming increasingly important in a variety of applications. In this paper, we ...
Qiang Fu, Arindam Banerjee
NIPS
2001
13 years 8 months ago
Covariance Kernels from Bayesian Generative Models
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task da...
Matthias Seeger
BMCBI
2008
228views more  BMCBI 2008»
13 years 7 months ago
Adaptive diffusion kernel learning from biological networks for protein function prediction
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...
Liang Sun, Shuiwang Ji, Jieping Ye