{"title":"RBF networks for density estimation","authors":"L. Sardo, J. Kittler","doi":"10.5281/ZENODO.36129","DOIUrl":null,"url":null,"abstract":"A non-parametric probability density function (pdf) estimation technique is presented. The estimation consists in approximating the unknown pdf by a network of Gaussian Radial Basis Functions (GRBFs). Complexity analysis is introduced in order to select the optimal number of GRBFs. Results obtained on real data show the potentiality of this technique.","PeriodicalId":282153,"journal":{"name":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1996 8th European Signal Processing Conference (EUSIPCO 1996)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.36129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
A non-parametric probability density function (pdf) estimation technique is presented. The estimation consists in approximating the unknown pdf by a network of Gaussian Radial Basis Functions (GRBFs). Complexity analysis is introduced in order to select the optimal number of GRBFs. Results obtained on real data show the potentiality of this technique.
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