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» Active learning in heteroscedastic noise
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KDD
2009
ACM
227views Data Mining» more  KDD 2009»
14 years 10 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
AMAI
2002
Springer
13 years 9 months ago
Minimizing Output Error in Multi-Layer Perceptrons
act It is well-established that a multi-layer perceptron (MLP) with a single hidden layer of N neurons and an activation function bounded by zero at negative infinity and one at in...
Jonathan P. Bernick
ICPPW
2006
IEEE
14 years 3 months ago
m-LPN: An Approach Towards a Dependable Trust Model for Pervasive Computing Applications
Trust, the fundamental basis of ‘cooperation’ – one of the most important characteristics for the performance of pervasive ad hoc network-- is under serious threat with the ...
Munirul M. Haque, Sheikh Iqbal Ahamed
ICIP
2005
IEEE
14 years 11 months ago
Curve segmentation using directional information, relation to pattern detection
We propose an extension of the conformal (or geodesic) active contour framework in which the conformal factor depends not only on the position of the curve but also on the directi...
Eric Pichon, Allen Tannenbaum
NCA
2006
IEEE
13 years 9 months ago
Evolutionary training of hardware realizable multilayer perceptrons
The use of multilayer perceptrons (MLP) with threshold functions (binary step function activations) greatly reduces the complexity of the hardware implementation of neural networks...
Vassilis P. Plagianakos, George D. Magoulas, Micha...