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» Optimal feature selection for support vector machines
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IWDW
2005
Springer
16 years 2 hour ago
Towards Multi-class Blind Steganalyzer for JPEG Images
In this paper, we use the previously proposed calibrated DCT features [9] to construct a Support Vector Machine classifier for JPEG images capable of recognizing which steganograp...
Tomás Pevný, Jessica J. Fridrich
ICML
2008
IEEE
16 years 7 months ago
Dirichlet component analysis: feature extraction for compositional data
We consider feature extraction (dimensionality reduction) for compositional data, where the data vectors are constrained to be positive and constant-sum. In real-world problems, t...
Hua-Yan Wang, Qiang Yang, Hong Qin, Hongbin Zha
AUSAI
2008
Springer
15 years 8 months ago
Character Recognition Using Hierarchical Vector Quantization and Temporal Pooling
In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neocortex and artificial intelligence models of mac...
John Thornton, Jolon Faichney, Michael Blumenstein...
ISNN
2005
Springer
16 years 5 hour ago
Multiple Parameter Selection for LS-SVM Using Smooth Leave-One-Out Error
In least squares support vector (LS-SVM), the key challenge lies in the selection of free parameters such as kernel parameters and tradeoff parameter. However, when a large number ...
Liefeng Bo, Ling Wang, Licheng Jiao
ICPR
2004
IEEE
16 years 7 months ago
An Efficient Three-Stage Classifier for Handwritten Digit Recognition
This paper proposes an efficient three-stage classifier for handwritten digit recognition based on NN (Neural Network) and SVM (Support Vector Machine) classifiers. The classifica...
Dejan Gorgevik, Dusan Cakmakov