TY - GEN
T1 - Learning cascaded reduced-set svms using linear programming
AU - Kim, Junae
AU - Shen, Chunhua
AU - Wang, Lei
PY - 2008
Y1 - 2008
N2 - This paper proposes a simple and efficient detection frame- work that uses reduced-set kernels. We first describe our approach which reduces the number of kernels. A con- vex optimization method is used for calculating the reduced sets. Following this, we propose a method that optimally designs the cascade. Our experimental results indicate that our method minimizes complexity regarding the number of kernels in the cascaded structure while preserving the low error rates. Our algorithm generates the optimal weight of kernels for each cascade stage. This proposed algorithm achieves high detection-rates at low computational cost.
AB - This paper proposes a simple and efficient detection frame- work that uses reduced-set kernels. We first describe our approach which reduces the number of kernels. A con- vex optimization method is used for calculating the reduced sets. Following this, we propose a method that optimally designs the cascade. Our experimental results indicate that our method minimizes complexity regarding the number of kernels in the cascaded structure while preserving the low error rates. Our algorithm generates the optimal weight of kernels for each cascade stage. This proposed algorithm achieves high detection-rates at low computational cost.
UR - http://www.scopus.com/inward/record.url?scp=67549088178&partnerID=8YFLogxK
U2 - 10.1109/DICTA.2008.49
DO - 10.1109/DICTA.2008.49
M3 - Conference contribution
SN - 9780769534565
T3 - Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008
SP - 619
EP - 626
BT - Proceedings - Digital Image Computing
T2 - Digital Image Computing: Techniques and Applications, DICTA 2008
Y2 - 1 December 2008 through 3 December 2008
ER -