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ICIC
2005
Springer
13 years 10 months ago
Methods of Decreasing the Number of Support Vectors via k-Mean Clustering
This paper proposes two methods which take advantage of k -mean clustering algorithm to decrease the number of support vectors (SVs) for the training of support vector machine (SVM...
Xiao-Lei Xia, Michael R. Lyu, Tat-Ming Lok, Guang-...
TMI
2002
143views more  TMI 2002»
13 years 4 months ago
A Support Vector Machine Approach for Detection of Microcalcifications
In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive ...
Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Niko...
ICPR
2006
IEEE
14 years 6 months ago
A Comparison of Texture Features Based on SVM and SOM
Experimental results of texture features derived from Gabor and other four wavelet transforms classified and clustered based on Support Vector Machine (SVMs) and Self-Organizing M...
Chaur-Chin Chen, Chien-Chang Chen, Chih-Ming Chen
ESANN
2008
13 years 6 months ago
Discrimination of regulatory DNA by SVM on the basis of over- and under-represented motifs
In this paper we apply three pattern recognition methods (support vector machine, cluster analysis and principal component analysis) to distinguish regulatory regions from coding a...
Rene te Boekhorst, Irina I. Abnizova, Lorenz Werni...
BMCBI
2006
143views more  BMCBI 2006»
13 years 5 months ago
IsoSVM - Distinguishing isoforms and paralogs on the protein level
Background: Recent progress in cDNA and EST sequencing is yielding a deluge of sequence data. Like database search results and proteome databases, this data gives rise to inferred...
Michael Spitzer, Stefan Lorkowski, Paul Cullen, Al...