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» Boosting for transfer learning with multiple sources
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CVPR
2010
IEEE
13 years 5 months ago
Boosting for transfer learning with multiple sources
Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. Th...
Yi Yao, Gianfranco Doretto
ICDM
2009
IEEE
137views Data Mining» more  ICDM 2009»
14 years 4 days 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
2010
IEEE
13 years 6 months ago
Boosting for Regression Transfer
The goal of transfer learning is to improve the learning of a new target concept given knowledge of related source concept(s). We introduce the first boosting-based algorithms for...
David Pardoe, Peter Stone
CIKM
2008
Springer
13 years 7 months ago
Transfer learning from multiple source domains via consensus regularization
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Ping Luo, Fuzhen Zhuang, Hui Xiong, Yuhong Xiong, ...
ICASSP
2011
IEEE
12 years 9 months ago
Feature selection based on Multiple Kernel Learning for single-channel sound source localization using the acoustic transfer fun
This paper presents a sound source (talker) localization method using only a single microphone. In our previous work [1], we discussed the single-channel sound source localization...
Ryoichi Takashima, Tetsuya Takiguchi, Yasuo Ariki