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ECCV
2008
Springer
16 years 4 months ago
Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks
Abstract. Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchi...
Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, Eric P. Xi...
108
Voted
DATE
2009
IEEE
107views Hardware» more  DATE 2009»
15 years 9 months ago
Learning early-stage platform dimensioning from late-stage timing verification
— Today's innovations in the automotive sector are, to a great extent, based on electronics. The increasing integration complexity and stringent cost reduction goals turn E/...
Kai Richter, Marek Jersak, Rolf Ernst
125
Voted
PAKDD
2010
ACM
212views Data Mining» more  PAKDD 2010»
15 years 7 months ago
Fast Perceptron Decision Tree Learning from Evolving Data Streams
Abstract. Mining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffdi...
Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer...
155
Voted
CVPR
2011
IEEE
14 years 10 months ago
Learning Object Color Models from Multi-view Constraints
Color is known to be highly discriminative for many object recognition tasks, but is difficult to infer from uncontrolled images in which the illuminant is not known. Traditional...
Trevor Owens, Kate Saenko, Trevor Darrell, Ayan Ch...
IANDC
2007
81views more  IANDC 2007»
15 years 2 months ago
Some natural conditions on incremental learning
The present study aims at insights into the nature of incremental learning in the context of Gold’s model of identification in the limit. With a focus on natural requirements s...
Sanjay Jain, Steffen Lange, Sandra Zilles