A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Particle swarm optimization for analysis of mass spectral serum profiles
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Mass spectrometry spectra are recognized as a screening tool for detecting discriminatory protein patterns. Mass spectra, however, are high dimensional data and a large number of local maxima (a.k.a. peaks ) have to be analyzed; to tackle this problem we have developed a three-step strategy. After data pre-processing we perform an unsupervised feature selection phase aimed at detecting salient parts of the spectra which could be useful for the subsequent classification phase. The main contribution of the paper is the development of this feature selection and extraction procedure grounded on the theory of multi-scale spaces. Then we use support vector machines for classification. Results obtained by the analysis of a data set of tumor/healthy samples allowed us to correctly classify more than 95% of samples. ROC analysis has been also performed.