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» Active Semi-Supervised Learning using Submodular Functions
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ICCV
2007
IEEE
14 years 9 months ago
Active Learning with Gaussian Processes for Object Categorization
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gauss...
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, Tr...
ICML
2003
IEEE
14 years 8 months ago
Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning
In the task of adaptive information filtering, a system receives a stream of documents but delivers only those that match a person's information need. As the system filters i...
Yi Zhang, Wei Xu, James P. Callan
BMCBI
2010
183views more  BMCBI 2010»
13 years 7 months ago
Active learning for human protein-protein interaction prediction
Background: Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is reso...
Thahir P. Mohamed, Jaime G. Carbonell, Madhavi Gan...
AO
2006
97views more  AO 2006»
13 years 7 months ago
An ontological model of device function: industrial deployment and lessons learned
Functionality is one of the key concepts of knowledge about artifacts. Functional knowledge shows a part of designer's intention (so-called design rationale), and thus its sha...
Yoshinobu Kitamura, Yusuke Koji, Riichiro Mizoguch...
ML
2007
ACM
144views Machine Learning» more  ML 2007»
13 years 7 months ago
Invariant kernel functions for pattern analysis and machine learning
In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invarianc...
Bernard Haasdonk, Hans Burkhardt