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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...
AAAI
2007
15 years 8 months ago
Learning and Inference for Hierarchically Split PCFGs
Treebank parsing can be seen as the search for an optimally refined grammar consistent with a coarse training treebank. We describe a method in which a minimal grammar is hierarc...
Slav Petrov, Dan Klein
NIPS
2004
15 years 7 months ago
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning
Log-concavity is an important property in the context of optimization, Laplace approximation, and sampling; Bayesian methods based on Gaussian process priors have become quite pop...
Liam Paninski
ADCM
2010
59views more  ADCM 2010»
15 years 6 months ago
Sampling inequalities for infinitely smooth functions, with applications to interpolation and machine learning
Sampling inequalities give a precise formulation of the fact that a differentiable function cannot attain large values, if its derivatives are bounded and if it is small on a suff...
Christian Rieger, Barbara Zwicknagl
CORR
2008
Springer
147views Education» more  CORR 2008»
15 years 6 months ago
A Minimum Relative Entropy Principle for Learning and Acting
This paper proposes a method to construct an adaptive agent that is universal with respect to a given class of experts, where each expert is designed specifically for a particular...
Pedro A. Ortega, Daniel A. Braun