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SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
11 years 10 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
ICDM
2009
IEEE
137views Data Mining» more  ICDM 2009»
14 years 2 months ago
Set-Based Boosting for Instance-Level Transfer
—The success of transfer to improve learning on a target task is highly dependent on the selected source data. Instance-based transfer methods reuse data from the source tasks to...
Eric Eaton, Marie desJardins
ICML
2003
IEEE
14 years 8 months ago
Learning with Knowledge from Multiple Experts
The use of domain knowledge in a learner can greatly improve the models it produces. However, high-quality expert knowledge is very difficult to obtain. Traditionally, researchers...
Matthew Richardson, Pedro Domingos
BMCBI
2008
100views more  BMCBI 2008»
13 years 7 months ago
High-precision high-coverage functional inference from integrated data sources
Background: Information obtained from diverse data sources can be combined in a principled manner using various machine learning methods to increase the reliability and range of k...
Bolan Linghu, Evan S. Snitkin, Dustin T. Holloway,...
ECCV
2010
Springer
14 years 3 hour ago
Learning to Recognize Objects from Unseen Modalities
Abstract. In this paper we investigate the problem of exploiting multiple sources of information for object recognition tasks when additional modalities that are not present in the...