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FOCS
1990
IEEE
15 years 10 months ago
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
Avrim Blum
ALT
2003
Springer
15 years 9 months ago
Efficiently Learning the Metric with Side-Information
Abstract. A crucial problem in machine learning is to choose an appropriate representation of data, in a way that emphasizes the relations we are interested in. In many cases this ...
Tijl De Bie, Michinari Momma, Nello Cristianini
NIPS
2000
15 years 7 months ago
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks
It has long been known that lateral inhibition in neural networks can lead to a winner-take-all competition, so that only a single neuron is active at a steady state. Here we show...
Xiaohui Xie, Richard H. R. Hahnloser, H. Sebastian...
BMCBI
2010
113views more  BMCBI 2010»
15 years 6 months ago
Unifying generative and discriminative learning principles
Background: The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of differe...
Jens Keilwagen, Jan Grau, Stefan Posch, Marc Stric...
CIKM
2010
Springer
15 years 4 months ago
Multi-view clustering with constraint propagation for learning with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, ma...
Eric Eaton, Marie desJardins, Sara Jacob