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• W2579782806 abstract "We apply Generalized Matrix Learning Vector Quantization (GMLVQ) to the classification of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) brain data. The aim is to achieve accurate detection and discrimination of neurodegenerative syndromes such as Parkinson’s Disease, Multiple System Atrophy and Progressive Supranuclear Palsy. Image data are pre-processed and analysed in terms of low-dimensional representations obtained by Principal Component Analysis in the Scaled Subprofile Model approach. The performance of the GMLVQ classifiers is evaluated in a Leave-One-Out framework. Comparison with earlier results shows that GMLVQ and Support Vector Machine with linear kernel achieve comparable performance while both outperform a C4-5 Decision Tree classifier." @default.
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• W2579782806 date "2016-01-01" @default.
• W2579782806 modified "2023-10-16" @default.
• W2579782806 title "Classification of FDG-PET Brain Data by Generalized Matrix Relevance LVQ" @default.
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• W2579782806 doi "https://doi.org/10.1007/978-3-319-50862-7_10" @default.
• W2579782806 hasPublicationYear "2016" @default.
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