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MLDM
2005
Springer
15 years 10 months ago
Unsupervised Learning of Visual Feature Hierarchies
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...
Fabien Scalzo, Justus H. Piater
130
Voted
SIGIR
2008
ACM
15 years 4 months ago
Learning to rank with partially-labeled data
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorith...
Kevin Duh, Katrin Kirchhoff
147
Voted
AAI
2005
138views more  AAI 2005»
15 years 4 months ago
Learning By Teaching: A New Agent Paradigm For Educational Software
This paper discusses Betty's Brain, a teachable agent in the domain of river ecosystems that combines learning by teaching with self-regulation mentoring to promote deep lear...
Gautam Biswas, Krittaya Leelawong, Daniel Schwartz...
ICASSP
2009
IEEE
15 years 2 months ago
Target detection using incremental learning on single-trial evoked response
The human neural responses associated with cognitive events, referred as event related potentials (ERPs), can provide reliable inference for target image detection. Incremental le...
Yonghong Huang, Deniz Erdogmus, Misha Pavel, Kenne...
BMCBI
2007
157views more  BMCBI 2007»
15 years 5 months ago
Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational
Background: High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination wi...
Nico Pfeifer, Andreas Leinenbach, Christian G. Hub...