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» Optimal feature selection for support vector machines
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ICML
2009
IEEE
14 years 8 months ago
A simpler unified analysis of budget perceptrons
The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing ...
Ilya Sutskever
MIR
2004
ACM
171views Multimedia» more  MIR 2004»
14 years 1 months ago
Mean version space: a new active learning method for content-based image retrieval
In content-based image retrieval, relevance feedback has been introduced to narrow the gap between low-level image feature and high-level semantic concept. Furthermore, to speed u...
Jingrui He, Hanghang Tong, Mingjing Li, HongJiang ...
EUROMICRO
2006
IEEE
14 years 2 months ago
Value-Based Selection of Requirements Engineering Tool Support
In large software and systems engineering companies like Siemens PSE there are several requirements tools in use. There is no “one tool fits all projects/departments” solution...
Matthias Heindl, Franz Reinisch, Stefan Biffl, Ale...
JMLR
2002
115views more  JMLR 2002»
13 years 7 months ago
PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to support vector machines....
Matthias Seeger
ICIAP
2009
ACM
14 years 8 months ago
A New Generative Feature Set Based on Entropy Distance for Discriminative Classification
Abstract. Score functions induced by generative models extract fixeddimensions feature vectors from different-length data observations by subsuming the process of data generation, ...
Alessandro Perina, Marco Cristani, Umberto Castell...