Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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In this paper a robust regions-of-suspicion (ROS) diagnosis system on mammograms, recognizing all types of abnormalities is presented and evaluated. A new type of statistical descriptors, based on Independent Component Analysis (ICA), derive the source regions that generate the observed ROS in mammograms. The reduced set of linear transformation coefficients, estimated from ICA after principal component analysis (PCA), compose the features vector that describes the observed regions in an effective way. The ROS are diagnosed using support-vector-machines (SVMs) with polynomial and radial basis function kernels. Taking into account the small number of training data, the PCA preprocessing step reduces the dimensionality of the features vector and consequently improves the classification accuracy of the SVM classifier. Extensive experiments using the Mammographic Image Analysis Society (MIAS) database have given high recognition accuracy above 87%.