Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients Export

@incollection{citeulike:2242723, abstract = {This paper presents an efficient approach for supporting decremental learning for incremental proximal support vector machines (SVM). The presented decremental algorithm based on decay coefficients is compared with an existing window-based decremental algorithm, and is shown to perform at a similar level in accuracy, but providing significantly better computational performance.}, author = {Tveit, Amund and Hetland, Magnus and Engum, H\r{a}avard}, citeulike-article-id = {2242723}, citeulike-linkout-0 = {http://www.springerlink.com/content/blx1qm2de93n2cr1}, journal = {Data Warehousing and Knowledge Discovery}, keywords = {classification, incremental, svm}, pages = {422--423}, posted-at = {2008-01-17 04:29:51}, priority = {2}, title = {Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients}, url = {http://www.springerlink.com/content/blx1qm2de93n2cr1}, year = {2003} }

TY - CHAP ID - citeulike:2242723 L3 - citeulike-article-id:2242723 N2 - This paper presents an efficient approach for supporting decremental learning for incremental proximal support vector machines (SVM). The presented decremental algorithm based on decay coefficients is compared with an existing window-based decremental algorithm, and is shown to perform at a similar level in accuracy, but providing significantly better computational performance. JF - Data Warehousing and Knowledge Discovery EP - 423 TI - Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients SP - 422 KW - classification KW - incremental KW - svm AU - Tveit, Amund AU - Hetland, Magnus AU - Engum, Håavard PY - 2003/// UR - http://www.springerlink.com/content/blx1qm2de93n2cr1 ER -