Second-order SMO improves SVM online and active learning
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Second-order SMO improves SVM online and active learning. / Glasmachers, Tobias; Igel, Christian.
In: Neural Computation, Vol. 20, No. 2, 2008, p. 374-382.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Second-order SMO improves SVM online and active learning
AU - Glasmachers, Tobias
AU - Igel, Christian
PY - 2008
Y1 - 2008
N2 - Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow online and active learning. Second, for large data sets, computing the exact SVM solution may be too time-consuming, and an efficient approximation can be preferable. The powerful LASVM iteratively approaches the exact SVM solution using sequential minimal optimization (SMO). It allows efficient online and active learning. Here, this algorithm is considerably improved in speed and accuracy by replacing the working set selection in the SMO steps. A second-order working set selection strategy, which greedily aims at maximizing the progress in each single step, is incorporated.
AB - Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow online and active learning. Second, for large data sets, computing the exact SVM solution may be too time-consuming, and an efficient approximation can be preferable. The powerful LASVM iteratively approaches the exact SVM solution using sequential minimal optimization (SMO). It allows efficient online and active learning. Here, this algorithm is considerably improved in speed and accuracy by replacing the working set selection in the SMO steps. A second-order working set selection strategy, which greedily aims at maximizing the progress in each single step, is incorporated.
U2 - 10.1162/neco.2007.10-06-354
DO - 10.1162/neco.2007.10-06-354
M3 - Journal article
C2 - 18045012
VL - 20
SP - 374
EP - 382
JO - Neural Computation
JF - Neural Computation
SN - 0899-7667
IS - 2
ER -
ID: 32645826