untitled
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<identifier>oai:HAL:hal-01165925v1</identifier>
<datestamp>2015-06-22</datestamp>
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<setSpec>subject:info</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:IGS2015</setSpec>
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<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Universum Learning for Semi-Supervised Signature Recognition from Spatio-Temporal Data</title>
<creator>Tencer, Lukas</creator>
<creator>Režnáková, Marta</creator>
<creator>Cheriet, Mohamed</creator>
<contributor>Département de génie de la production automatisée ; Ecole de Technologie Supérieure [Montréal] (ETS)</contributor>
<description>International audience</description>
<source>17th Biennial Conference of the International Graphonomics Society</source>
<coverage>Pointe-à-Pitre, Guadeloupe</coverage>
<contributor>International Graphonomics Society (IGS)</contributor>
<contributor>Université des Antilles (UA)</contributor>
<contributor>Céline Rémi</contributor>
<contributor>Lionel Prévost</contributor>
<contributor>Eric Anquetil</contributor>
<identifier>hal-01165925</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165925</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165925/document</identifier>
<identifier>https://hal.univ-antilles.fr/hal-01165925/file/IGS_2015_submission_38.pdf</identifier>
<source>https://hal.univ-antilles.fr/hal-01165925</source>
<source>Céline Rémi; Lionel Prévost; Eric Anquetil. 17th Biennial Conference of the International Graphonomics Society, Jun 2015, Pointe-à-Pitre, Guadeloupe. 2015, Drawing, Handwriting Processing Analysis: New Advances and Challenges</source>
<language>en</language>
<subject lang=en>spatio-temporal data</subject>
<subject lang=en>Universum learning</subject>
<subject lang=en>Universum data</subject>
<subject>[INFO] Computer Science [cs]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>We present a novel approach towards signature recognition from spatio-temporal data. The data is obtained by recording gyroscope and accelerometer measurements from an embedded pen device. The idea of Universum learning was previously presented by Vapnik and recently popularized in machine learning community. It assumes that the decision boundary of a classifier lies close to data with high uncertainty. The quality of the final classifier strongly depends on a way how to choose the Universum data and also on the representation of original data. In our paper we use a novel approach of Universum learning to classify signature data, also we present our novel idea how to sample the Universum data. At last, we also find more effective representation of the signature data itself compared to the baseline method. These three novelties allow us to outperform previously published results by 4.89% / 5.58%.</description>
<date>2015-06-21</date>
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