Multiple kernel learning, conic duality, and the SMO algorithm

  title={Multiple kernel learning, conic duality, and the SMO algorithm},
  author={Francis R. Bach and Gert R. G. Lanckriet and Michael I. Jordan},
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to a convex optimization problem known as a quadratically-constrained quadratic program (QCQP). Unfortunately, current convex optimization… CONTINUE READING
Highly Influential
This paper has highly influenced 171 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 2,012 citations. REVIEW CITATIONS
906 Extracted Citations
5 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 906 extracted citations

2,013 Citations

Citations per Year
Semantic Scholar estimates that this publication has 2,013 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-5 of 5 references

The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm

  • E. D. Andersen, K. D. Andersen
  • High Perf. Optimization (pp. 197–232)
  • 2000
Highly Influential
8 Excerpts

Making large-scale support vector machine learning practical

  • T. Joachims
  • Advances in Kernel Methods: Support Vector…
  • 1998
Highly Influential
9 Excerpts

Similar Papers

Loading similar papers…