Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/67410
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Type: Conference paper
Title: Learning cascaded reduced - set SVMs using linear programming
Author: Kim, J.
Shen, C.
Wang, L.
Citation: Digital Image Computing : Techniques and Applications (DICTA'08), 1-3 December, 2008; pp. 619-62
Publisher: IEEE
Publisher Place: Online
Issue Date: 2008
ISBN: 9780769534565
Conference Name: International Conference on Digital Image Computing - Techniques and Applications, (2008 : Canberra, ACT, Australia)
Statement of
Responsibility: 
Junae Kim, Chunhua Shen and Lei Wang
Abstract: This paper proposes a simple and efficient detection framework that uses reduced-set kernels. We first describe our approach which reduces the number of kernels. A convex 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.
Rights: Copyright © 2008 by The Institute of Electrical and Electronics Engineers, Inc. – All Rights Reserved
RMID: 0020112861
DOI: 10.1109/DICTA.2008.49
Appears in Collections:Computer Science publications

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