Van Heerden, CBarnard, E2010-01-192010-01-192009-11Van Heerden, C and Barnard, E. 2009. Combining multiple classifiers for age classification. 20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Stellenbosch, South Africa, 30 November-01 December 2009, pp 59-64978-0-7992-2356-9http://hdl.handle.net/10204/390420th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Stellenbosch, South Africa, 30 November-01 December 2009The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector machines (SVMs) are trained on two different types of feature classes to estimate posterior class probabilities. The posteriors from these classifiers are combined using different combination rules and functions described in the literature. A novel age classifier is also developed by using an SVM to predict posterior class probabilities using two different types of classifier outputs; gender classification results and regression age estimates. The authors show that for combining posterior probabilities, simple combination rules such as the product rule perform surprisingly well as opposed to trainable combination strategies that require a significant amount of data and training effortenAge classificationSupport vector machinePRASA 2009Conference PresentationVan Heerden, C., & Barnard, E. (2009). Combining multiple classifiers for age classification. PRASA 2009. http://hdl.handle.net/10204/3904Van Heerden, C, and E Barnard. "Combining multiple classifiers for age classification." (2009): http://hdl.handle.net/10204/3904Van Heerden C, Barnard E, Combining multiple classifiers for age classification; PRASA 2009; 2009. http://hdl.handle.net/10204/3904 .TY - Conference Presentation AU - Van Heerden, C AU - Barnard, E AB - The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector machines (SVMs) are trained on two different types of feature classes to estimate posterior class probabilities. The posteriors from these classifiers are combined using different combination rules and functions described in the literature. A novel age classifier is also developed by using an SVM to predict posterior class probabilities using two different types of classifier outputs; gender classification results and regression age estimates. The authors show that for combining posterior probabilities, simple combination rules such as the product rule perform surprisingly well as opposed to trainable combination strategies that require a significant amount of data and training effort DA - 2009-11 DB - ResearchSpace DP - CSIR KW - Age classification KW - Support vector machine KW - PRASA 2009 LK - https://researchspace.csir.co.za PY - 2009 SM - 978-0-7992-2356-9 T1 - Combining multiple classifiers for age classification TI - Combining multiple classifiers for age classification UR - http://hdl.handle.net/10204/3904 ER -