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» Evaluating learning algorithms and classifiers
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UAI
2008
15 years 2 months ago
Small Sample Inference for Generalization Error in Classification Using the CUD Bound
Confidence measures for the generalization error are crucial when small training samples are used to construct classifiers. A common approach is to estimate the generalization err...
Eric Laber, Susan Murphy
FGR
2004
IEEE
118views Biometrics» more  FGR 2004»
15 years 4 months ago
Components for Face Recognition
We present a method for automatically learning a set of discriminatory facial components for face recognition. The algorithm performs an iterative growing of components starting w...
Bernd Heisele, Takamasa Koshizen
102
Voted
EDM
2008
136views Data Mining» more  EDM 2008»
15 years 2 months ago
Machine Classification of Peer Comments in Physics
As part of an ongoing project where SWoRD, a Web-based reciprocal peer review system, is used to support disciplinary writing, this study reports machine learning classifications o...
Kwangsu Cho
87
Voted
NIPS
2003
15 years 1 months ago
Minimising Contrastive Divergence in Noisy, Mixed-mode VLSI Neurons
This paper presents VLSI circuits with continuous-valued probabilistic behaviour realized by injecting noise into each computing unit(neuron). Interconnecting the noisy neurons fo...
Hsin Chen, Patrice Fleury, Alan F. Murray
92
Voted
ANLP
2000
123views more  ANLP 2000»
15 years 1 months ago
Noun Phrase Recognition by System Combination
The performance of machine learning algorithms can be improved by combining the output of different systems. In this paper we apply this idea to the recognition of noun phrases. W...
Erik F. Tjong Kim Sang