Approximation of the gradient of the error probability for vector quantizers

Claudia Diamantini, Laura Genga, Domenico Potena

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

Vector Quantizers (VQ) can be exploited for classification. In particular the gradient of the error probability performed by a VQ with respect to the position of its code vectors can be formally derived, hence the optimum VQ can be theoretically found. Unfortunately, this equation is of limited use in practice, since it relies on the knowledge of the class conditional probability distributions. In order to apply the method to real problems where distributions are unknown, a stochastic approximation has been previously proposed to derive a practical learning algorithm. In this paper we relax some of the assumptions underlying the original proposal and study the advantages of the resulting algorithm by both synthetic and real case studies.
Originele taal-2Engels
Titel20th Italian Symposium on Advanced Database Systems
Pagina's193-204
Aantal pagina's12
StatusGepubliceerd - 24 jun. 2012
Extern gepubliceerdJa
Evenement20th Italian Symposium on Advanced Database Systems (SEBD 2012) - Venice, Italië
Duur: 24 jun. 201227 jun. 2012

Congres

Congres20th Italian Symposium on Advanced Database Systems (SEBD 2012)
Land/RegioItalië
StadVenice
Periode24/06/1227/06/12

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