Multiple kernel learning, conic duality, and the SMO algorithm
Multiple kernel learning, conic duality, and the SMO algorithm
F. Bach,Gert R. G. Lanckriet,Michael I. Jordan
2004 · DOI: 10.1145/1015330.1015424
International Conference on Machine Learning · 1,667 Citations
TLDR
Experimental results are presented that show that the proposed novel dual formulation of the QCQP as a second-order cone programming problem is significantly more efficient than the general-purpose interior point methods available in current optimization toolboxes.