TY - GEN
T1 - Boosting of Support Vector Machines with application to editing
AU - Rangel, Pedro A
AU - Lozano, Fernando
AU - García, Elkin
PY - 2005/12/1
Y1 - 2005/12/1
N2 - In this paper, we present a weakened variation of Support Vector Machines that can be used together with Adaboost. Our modified Support Vector Machine algorithm has the following interesting properties: First, it is able to handle distributions over the training data. Second, it is a weak algorithm in the sense that it ensures an empirical error upper bounded by 1/2. Third, when used together with Adaboost, the resulting algorithm is faster than the usual SVM training algorithm. Finally, we show that our boosted SVM can be effective as an editing algorithm.
AB - In this paper, we present a weakened variation of Support Vector Machines that can be used together with Adaboost. Our modified Support Vector Machine algorithm has the following interesting properties: First, it is able to handle distributions over the training data. Second, it is a weak algorithm in the sense that it ensures an empirical error upper bounded by 1/2. Third, when used together with Adaboost, the resulting algorithm is faster than the usual SVM training algorithm. Finally, we show that our boosted SVM can be effective as an editing algorithm.
UR - http://www.scopus.com/inward/record.url?scp=33847331702&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847331702&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2005.13
DO - 10.1109/ICMLA.2005.13
M3 - Conference contribution
AN - SCOPUS:33847331702
SN - 0769524958
SN - 9780769524955
T3 - Proceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applications
SP - 374
EP - 379
BT - Proceedings - ICMLA 2005
T2 - ICMLA 2005: 4th International Conference on Machine Learning and Applications
Y2 - 15 December 2005 through 17 December 2005
ER -