<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mihály Gara</style></author><author><style face="normal" font="default" size="100%">Tamás Sámuel Tasi</style></author><author><style face="normal" font="default" size="100%">Péter Balázs</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Annick Montanvert</style></author><author><style face="normal" font="default" size="100%">Pierre Soille</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine learning as a preprocessing phase in discrete tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Applications of Discrete Geometry and Mathematical Morphology (WADGMM)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title><short-title><style face="normal" font="default" size="100%">LNCS</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Aug 2012</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7346</style></number><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin; Heidelberg; New York; London; Paris; Tokyo</style></pub-location><pages><style face="normal" font="default" size="100%">109 - 124</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper we investigate for two well-known machine learning methods, decision trees and neural networks, how they classify discrete images from their projections. As an example, we present classification results when the task is to guess the number of intensity values of the discrete image. Machine learning can be used in Discrete Tomography as a preprocessing step in order to choose the proper reconstruction algorithm or - with the aid of the knowledge acquired - to improve its accuracy. We also show how to design new evolutionary reconstruction methods that can exploit the information gained by machine learning classifiers. © 2012 Springer-Verlag.&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type><notes><style face="normal" font="default" size="100%">ScopusID: 84865454250doi: 10.1007/978-3-642-32313-3_8</style></notes></record></records></xml>