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ICONIP
2009
15 years 5 months ago
Learning Gaussian Process Models from Uncertain Data
It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
Patrick Dallaire, Camille Besse, Brahim Chaib-draa
TEI
2009
ACM
141views Hardware» more  TEI 2009»
16 years 2 months ago
Kurio: a museum guide for families
We discuss three design strategies for improving the quality of social interaction and learning with interactive museum guides: 1) embodied interaction; 2) game-learning; 3) a hyb...
Ron Wakkary, Marek Hatala, Kevin Muise, Karen Tane...
ACCV
2009
Springer
16 years 1 months ago
Interactive Super-Resolution through Neighbor Embedding
Learning based super-resolution can recover high resolution image with high quality. However, building an interactive learning based super-resolution system for general images is e...
Jian Pu, Junping Zhang, Peihong Guo, Xiaoru Yuan
GECCO
2005
Springer
129views Optimization» more  GECCO 2005»
16 years 23 days ago
Post-processing clustering to reduce XCS variability
XCS is a stochastic algorithm, so it does not guarantee to produce the same results when run with the same input. When interpretability matters, obtaining a single, stable result ...
Flavio Baronti, Alessandro Passaro, Antonina Stari...
GECCO
2007
Springer
144views Optimization» more  GECCO 2007»
15 years 11 months ago
Mixing independent classifiers
In this study we deal with the mixing problem, which concerns combining the prediction of independently trained local models to form a global prediction. We deal with it from the ...
Jan Drugowitsch, Alwyn Barry