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» Hybrid System for Generating Learning Object Metadata
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RECSYS
2009
ACM
14 years 2 months ago
Recommending new movies: even a few ratings are more valuable than metadata
In the Netflix Prize competition many new collaborative filtering (CF) approaches emerged, which are excellent in optimizing the RMSE of the predictions. Matrix factorization (M...
István Pilászy, Domonkos Tikk
ICCV
2009
IEEE
13 years 6 months ago
A hybrid generative/discriminative classification framework based on free-energy terms
Hybrid generative-discriminative techniques and, in particular, generative score-space classification methods have proven to be valuable approaches in tackling difficult object or...
Alessandro Perina, Marco Cristani, Umberto Castell...
NIPS
2008
13 years 10 months ago
Learning Hybrid Models for Image Annotation with Partially Labeled Data
Extensive labeled data for image annotation systems, which learn to assign class labels to image regions, is difficult to obtain. We explore a hybrid model framework for utilizing...
Xuming He, Richard S. Zemel
EXPERT
2008
134views more  EXPERT 2008»
13 years 8 months ago
Learning to Tag and Tagging to Learn: A Case Study on Wikipedia
Natural language technologies have been long envisioned to play a crucial role in transitioning from the current Web to a more "semantic" Web. If anything, the significa...
Peter Mika, Massimiliano Ciaramita, Hugo Zaragoza,...
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
14 years 1 months ago
TestFul: using a hybrid evolutionary algorithm for testing stateful systems
This paper introduces TestFul, a framework for testing stateful systems and focuses on object-oriented software. TestFul employs a hybrid multi-objective evolutionary algorithm, t...
Matteo Miraz, Pier Luca Lanzi, Luciano Baresi