eprintid: 3145
rev_number: 10
eprint_status: archive
userid: 69
dir: disk0/00/00/31/45
datestamp: 2016-02-26 15:47:40
lastmod: 2016-03-01 10:41:10
status_changed: 2016-02-26 15:47:40
type: monograph
metadata_visibility: show
creators_name: Morisi, Rita
creators_name: Gnecco, Giorgio
creators_name: Lanconelli, Nico
creators_name: Zanigni, Stefano
creators_name: Manners, David Neil
creators_name: Testa, Claudia
creators_name: Evangelisti, Stefania
creators_name: Gramegna, Laura Ludovica
creators_name: Bianchini, Claudio
creators_name: Cortelli, Pietro
creators_name: Tonon, Caterina
creators_name: Lodi, Raffaele
creators_id: rita.morisi@imtlucca.it
creators_id: giorgio.gnecco@imtlucca.it
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: 
creators_id: 
title: Binary and
multi-class Parkinsonian disorders classification using Support Vector Machines with
graph-based features
ispublished: submitted
subjects: QA75
subjects: RC0321
divisions: CSA
full_text_status: none
monograph_type: working_paper
date: 2016
date_type: submitted
institution: IMT Institute for Advanced Studies Lucca
refereed: FALSE
citation:   Morisi, Rita and Gnecco, Giorgio and Lanconelli, Nico and Zanigni, Stefano and Manners, David Neil and Testa, Claudia and Evangelisti, Stefania and Gramegna, Laura Ludovica and Bianchini, Claudio and Cortelli, Pietro and Tonon, Caterina and Lodi, Raffaele  Binary and multi-class Parkinsonian disorders classification using Support Vector Machines with graph-based features.  Working Paper       (Submitted)