QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines (2006)

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by Don Hush , Patrick Kelly , Clint Scovel , Ingo Steinwart

Venue:	JOURNAL OF MACHINE LEARNING RESEARCH
Citations:	10 - 1 self

BibTeX

@ARTICLE{Hush06qpalgorithms,
    author = {Don Hush and Patrick Kelly and Clint Scovel and Ingo Steinwart},
    title = { QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines},
    journal = {JOURNAL OF MACHINE LEARNING RESEARCH},
    year = {2006},
    volume = {7},
    pages = {733--769}
}

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Abstract

We describe polynomial-time algorithms that produce approximate solutions with guaranteed accuracy for a class of QP problems that are used in the design of support vector machine classifiers. These

Citations

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56	A Fast Iterative Nearest Point Algorithm for Support Vector Machine Classifier Design - Keerthi, Shevade, et al. - 2000
41	Convergence of a generalized SMO algorithm for SVM classifier design - Keerthi, Gilbert
34	A classification framework for anomaly detection - Steinwart, Hush, et al. - 2005
26	Fast Rates for Support Vector Machines - Scovel, Steinwart - 2004
18	O.: Provably fast training algorithms for support vector machines - Balcázar, Dai, et al. - 2001
14	A Study of SMO-Type Decomposition Methods for Support Vector Machines - Chen, Lin
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4	General polynomial time decomposition algorithms - List, Simon - 2005
3	On the complexity of working set selection - Simon
2	Stability of unstable learning algorithms - Hush, Scovel, et al. - 2003
2	A note on the decomposition methods for support vector regression - Liao, Lin, et al.
2	Learning rates for density level detection - Steinwart, Hush, et al. - 2005
1	1: Offset(g,y,u) 2: 3: s + ← ∑i:yi=1 ui, s − ← 0 4: � ( ¯g1, ¯y1,ū1),...,( ¯gn, ¯yn,ūn) � ←SortIncreasing � (g1,y1,u1),...,(gn,yn,un) � 5: L ← ∑i: ¯yi=1 ūi( ¯gi − ¯g1) 6: L ∗ ← L, b ← ¯g1 7: for (i = 1,...,n − 1) do 8: if ( ¯yi = 1) then 9: s + ← s + − ūi - Chen, Fan, et al. - 2005
1	A simple decomposition algorithm for support vector machines - Hsu, Lin
1	Alamos National Laboratory LA-UR-03-4845 - report, Los - 2003
1	Polynomial time algorithms for computing approximate SVM solutions with guaranteed accuracy - Hush, Scovel, et al. - 2005
1	Feasible direction decomposition algorithms for training support vector machines - HUSH, SCOVEL, et al.
1	A general convergence theorem for the desomposition method - List, Simon - 2004
1	Approximate duality - Scovel, Hush, et al.