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» On the Complexity of Function Learning
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87
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GECCO
2006
Springer
157views Optimization» more  GECCO 2006»
15 years 4 months ago
gLINC: identifying composability using group perturbation
We present two novel perturbation-based linkage learning algorithms that extend LINC [5]; a version of LINC optimised for decomposition tasks (oLINC) and a hierarchical version of...
David Jonathan Coffin, Christopher D. Clack
STOC
2012
ACM
221views Algorithms» more  STOC 2012»
13 years 2 months ago
From query complexity to computational complexity
We consider submodular optimization problems, and provide a general way of translating oracle inapproximability results arising from the symmetry gap technique to computational co...
Shahar Dobzinski, Jan Vondrák
ALT
1999
Springer
15 years 4 months ago
PAC Learning with Nasty Noise
We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. Th...
Nader H. Bshouty, Nadav Eiron, Eyal Kushilevitz
99
Voted
BMCBI
2006
216views more  BMCBI 2006»
15 years 15 days ago
Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data
Background: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfu...
Haiying Wang, Huiru Zheng, David Simpson, Francisc...
116
Voted
NIPS
2001
15 years 1 months ago
Algorithmic Luckiness
Classical statistical learning theory studies the generalisation performance of machine learning algorithms rather indirectly. One of the main detours is that algorithms are studi...
Ralf Herbrich, Robert C. Williamson