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» On learning with dissimilarity functions
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EUROGP
2010
Springer
166views Optimization» more  EUROGP 2010»
15 years 11 months ago
Learning a Lot from Only a Little: Genetic Programming for Panel Segmentation on Sparse Sensory Evaluation Data
We describe a data mining framework that derives panelist information from sparse flavour survey data. One component of the framework executes genetic programming ensemble based s...
Katya Vladislavleva, Kalyan Veeramachaneni, Una-Ma...
ISCAS
1999
IEEE
73views Hardware» more  ISCAS 1999»
15 years 11 months ago
Correlation learning rule in floating-gate pFET synapses
We study the weight dynamics of the floating-gate pFET synapse and the effects of the pFET's gate and drain voltages on these dynamics. We show that we can derive a weight upd...
Paul E. Hasler, Jeff Dugger
GECCO
2006
Springer
167views Optimization» more  GECCO 2006»
15 years 10 months ago
Genomic computing networks learn complex POMDPs
A genomic computing network is a variant of a neural network for which a genome encodes all aspects, both structural and functional, of the network. The genome is evolved by a gen...
David J. Montana, Eric Van Wyk, Marshall Brinn, Jo...
ECIR
2007
Springer
15 years 8 months ago
A Bayesian Approach for Learning Document Type Relevance
Retrieval accuracy can be improved by considering which document type should be filtered out and which should be ranked higher in the result list. Hence, document type can be used...
Peter C. K. Yeung, Stefan Büttcher, Charles L...
EMNLP
2006
15 years 8 months ago
Learning Field Compatibilities to Extract Database Records from Unstructured Text
Named-entity recognition systems extract entities such as people, organizations, and locations from unstructured text. Rather than extract these mentions in isolation, this paper ...
Michael L. Wick, Aron Culotta, Andrew McCallum