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» Self-taught learning: transfer learning from unlabeled data
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ICML
2006
IEEE
14 years 8 months ago
Learning a kernel function for classification with small training samples
When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. Thi...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
COLT
2008
Springer
13 years 9 months ago
An Information Theoretic Framework for Multi-view Learning
In the multi-view learning paradigm, the input variable is partitioned into two different views X1 and X2 and there is a target variable Y of interest. The underlying assumption i...
Karthik Sridharan, Sham M. Kakade
CVPR
2010
IEEE
13 years 7 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
MOBISYS
2007
ACM
14 years 7 months ago
Context-for-wireless: context-sensitive energy-efficient wireless data transfer
Ubiquitous connectivity on mobile devices will enable numerous new applications in healthcare and multimedia. We set out to check how close we are towards ubiquitous connectivity ...
Ahmad Rahmati, Lin Zhong
ASUNAM
2010
IEEE
13 years 9 months ago
Semi-Supervised Classification of Network Data Using Very Few Labels
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Pr...
Frank Lin, William W. Cohen