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» Normalization in Support Vector Machines
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ICASSP
2010
IEEE
15 years 3 months ago
Bayesian compressive sensing for phonetic classification
In this paper, we introduce a novel bayesian compressive sensing (CS) technique for phonetic classification. CS is often used to characterize a signal from a few support training...
Tara N. Sainath, Avishy Carmi, Dimitri Kanevsky, B...
150
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JCP
2007
154views more  JCP 2007»
15 years 4 months ago
Partially Reconfigurable Vector Processor for Embedded Applications
—Embedded systems normally involve a combination of hardware and software resources designed to perform dedicated tasks. Such systems have widely crept into industrial control, a...
Muhammad Z. Hasan, Sotirios G. Ziavras
ICIAR
2005
Springer
15 years 10 months ago
On the Individuality of the Iris Biometric
We consider quantitatively establishing the discriminative power of iris biometric data. It is difficult, however, to establish that any biometric modality is capable of distingui...
Sungsoo Yoon, Seung-Seok Choi, Sung-Hyuk Cha, Yill...
CMIG
2010
110views more  CMIG 2010»
14 years 11 months ago
Unsupervised SVM-based gridding for DNA microarray images
This paper presents a novel method for unsupervised DNA microarray gridding based on Support Vector Machines (SVMs). Each spot is a small region on the microarray surface where cha...
Dimitris G. Bariamis, Dimitris Maroulis, Dimitrios...
PKDD
2009
Springer
88views Data Mining» more  PKDD 2009»
15 years 11 months ago
Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs
The Support Vector Machine error bound is a function of the margin and radius. Standard SVM algorithms maximize the margin within a given feature space, therefore the radius is fi...
Huyen Do, Alexandros Kalousis, Melanie Hilario