Machine Learning
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On the Generalization Ability of GRLVQ Networks
Neural Processing Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Adaptive relevance matrices in learning vector quantization
Neural Computation
Regularization in matrix relevance learning
IEEE Transactions on Neural Networks
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Objective: The generalized matrix learning vector quantization (GMLVQ) is used to estimate the relevance of texture features in their ability to classify interstitial lung disease patterns in high-resolution computed tomography images. Methodology: After a stochastic gradient descent, the GMLVQ algorithm provides a discriminative distance measure of relevance factors, which can account for pairwise correlations between different texture features and their importance for the classification of healthy and diseased patterns. 65 texture features were extracted from gray-level co-occurrence matrices (GLCMs). These features were ranked and selected according to their relevance obtained by GMLVQ and, for comparison, to a mutual information (MI) criteria. The classification performance for different feature subsets was calculated for a k-nearest-neighbor (kNN) and a random forests classifier (RanForest), and support vector machines with a linear and a radial basis function kernel (SVMlin and SVMrbf). Results: For all classifiers, feature sets selected by the relevance ranking assessed by GMLVQ had a significantly better classification performance (p