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» Boosting for transfer learning with multiple sources
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CVPR
2010
IEEE
13 years 9 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 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
2010
IEEE
13 years 10 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 11 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
13 years 1 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