Abstract
The paper introduces a novel Direct L2 Support Vector Machine (DL2 SVM) classifier and presents the performances of its 4 variants on 12 different binary and multiclass datasets. Direct L2 SVM avoids solving quadratic programming (QP) problem and it solves the Nonnegative Least Squares (NNLS) task instead, which, unlike the related iterative algorithms, produces an impeccably accurate results. Solutions obtained by NNLS and QP are equal but NNLS needs much less CPU time. The comprehensive DL2 SVM model, as well as its three variants, are devised. The similarities with, and differences in respect to, LS SVM and proximal SVMs are pointed at too. The four DL2 SVM models performances are compared in terms of accuracy, percentage of support vectors and CPU time. A strict nested cross-validation (double resampling) is used in all experiments.
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Zigic, L., Kecman, V. (2014). Variants and Performances of Novel Direct Learning Algorithms for L2 Support Vector Machines. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_8
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DOI: https://doi.org/10.1007/978-3-319-07176-3_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07175-6
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