Fuzzy support vector machines for age and gender classification

Phuoc Nguyen, Trung Le, Dat Tran, Xu Huang, Dharmendra Sharma

Research output: Contribution to conferencePaperpeer-review

10 Citations (Scopus)

Abstract

Support vector machine (SVM) has been proven as a powerful tool for solving age and gender classification problems. However, SVM is sensitive to noise and outliers. In this paper we propose a new fuzzy SVM based on an assumption that training data points should not be treated equally to avoid the problem of sensitivity to noise and outliers. This can be achieved by assigning a fuzzy membership as a weight to each training data point. A method to calculate fuzzy memberships is also presented. Experiments performed on the aGender corpus for INTERSPEECH 2010 Paralinguistic Challenge show that the proposed fuzzy SVM can improve age and gender classification accuracy.

Original languageEnglish
Pages2806-2809
Number of pages4
Publication statusPublished - 1 Dec 2010
Externally publishedYes
EventAnnual Conference of the International Speech Communication Association (was Eurospeech) 2010 - Makuhari, Japan
Duration: 26 Sep 201030 Sep 2010
Conference number: 11th
http://www.interspeech2010.jpn.org/

Conference

ConferenceAnnual Conference of the International Speech Communication Association (was Eurospeech) 2010
Abbreviated titleInterspeech 2010
CountryJapan
CityMakuhari
Period26/09/1030/09/10
Internet address

Keywords

  • Age classification
  • Fuzzy support vector machine
  • Gender classification
  • Paralinguistic challenge

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