A Game-Theoretic Approach to Graph Transduction: An Experimental Study

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dc.contributor.advisor Pelillo, Marcello it_IT
dc.contributor.author Schiavinato, Michele <1986> it_IT
dc.date.accessioned 2014-02-05 it_IT
dc.date.accessioned 2014-03-29T10:45:45Z
dc.date.available 2015-04-07T13:58:29Z
dc.date.issued 2014-03-11 it_IT
dc.identifier.uri http://hdl.handle.net/10579/4381
dc.description.abstract An important class of problems in machine learning is based on semi-supervised process to look for consistent labeling for a set of objects. The crucial aspect which features this topic is how to spread the available knowledge on the data to infer a proper answer for the unknown one. In this dissertation we use an important graph based model of semi-supervised learning known as Graph Transduction. Our research begins from a novel solution in Game Theory which models the problem just a noncooperative game: the Graph Transduction Game (GTG). We study this algorithm for the specific instance of visual world, leading the analysis over the main information of visual similarity between images and measures of similarity projected to categories. Finally we introduce the implications arisen on large-scale classification, guessing a possible solution generalizing the current version of GTG, which can exploit of category similarities actively in the learning. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Michele Schiavinato, 2014 it_IT
dc.title A Game-Theoretic Approach to Graph Transduction: An Experimental Study it_IT
dc.title.alternative it_IT
dc.type Bachelor Thesis it_IT
dc.degree.name Informatica - computer science it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Scienze Ambientali, Informatica e Statistica it_IT
dc.description.academicyear 2012/2013, sessione straordinaria it_IT
dc.rights.accessrights openAccess it_IT
dc.thesis.matricno 810469 it_IT
dc.subject.miur INF/01 INFORMATICA it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.provenance.upload Michele Schiavinato (810469@stud.unive.it), 2014-02-05 it_IT
dc.provenance.plagiarycheck Marcello Pelillo (pelillo@unive.it), 2014-02-17 it_IT

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