DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hyun-Chul Kim | - |
dc.contributor.author | Kim, D | - |
dc.contributor.author | Zoubin Ghahramani | - |
dc.contributor.author | Sung Yang Bang | - |
dc.date.accessioned | 2016-04-01T01:58:21Z | - |
dc.date.available | 2016-04-01T01:58:21Z | - |
dc.date.created | 2009-08-19 | - |
dc.date.issued | 2006-04-15 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.other | 2006-OAK-0000005797 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/24123 | - |
dc.description.abstract | This paper concerns the gender classification task of discriminating between images of faces of men and women from face images. In appearance-based approaches, the initial images are preprocessed (e.g. normalized) and input into classifiers. Recently.. support vector machines (SVMs) which are popular kernel classifiers have been applied to gender classification and have shown excellent performance. SVMs have difficulty in determining the hyperparameters in kernels (using cross-validation). We propose to use Gaussian process classifiers (GPCs) which are Bayesian kernel classifiers. The main advantage of GPCs over SVMs is that they determine the hyperparameters of the kernel based on Bayesian model selection criterion. The experimental results show that our methods outperformed SVMs with cross-validation in most of data sets. Moreover, the kernel hyperparameters found by GPCs using Bayesian methods call be used to improve SVM performance. (c) 2005 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | PATTERN RECOGNITION LETTERS | - |
dc.subject | gender classification | - |
dc.subject | appearance-based gender classification | - |
dc.subject | kernel machines | - |
dc.subject | Gaussian process classifiers | - |
dc.subject | support vector machines | - |
dc.subject | FACES | - |
dc.title | Appearance-based gender classification with Gaussian processes | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1016/j.patrec.2005.09.027 | - |
dc.author.google | Kim, HC | - |
dc.author.google | Kim, D | - |
dc.author.google | Ghahramani, Z | - |
dc.author.google | Bang, SY | - |
dc.relation.volume | 27 | - |
dc.relation.issue | 6 | - |
dc.relation.startpage | 618 | - |
dc.relation.lastpage | 626 | - |
dc.contributor.id | 10054411 | - |
dc.relation.journal | PATTERN RECOGNITION LETTERS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION LETTERS, v.27, no.6, pp.618 - 626 | - |
dc.identifier.wosid | 000236286700013 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 626 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 618 | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 27 | - |
dc.contributor.affiliatedAuthor | Kim, D | - |
dc.contributor.affiliatedAuthor | Sung Yang Bang | - |
dc.identifier.scopusid | 2-s2.0-32844465043 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 34 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | gender classification | - |
dc.subject.keywordAuthor | appearance-based gender classification | - |
dc.subject.keywordAuthor | kernel machines | - |
dc.subject.keywordAuthor | Gaussian process classifiers | - |
dc.subject.keywordAuthor | support vector machines | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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