Multiclass multiple kernel learning

by Alexander Zien , Cheng Soon Ong

Venue:	In ICML. ACM
Citations:	26 - 3 self

Datum	Value	Source
TITLE	Multiclass multiple kernel learning	INFERENCE
AUTHOR NAME	Alexander Zien	SVM HeaderParse 0.2
AUTHOR NAME	Cheng Soon Ong	SVM HeaderParse 0.2
ABSTRACT	In many applications it is desirable to learn from several kernels. “Multiple kernel learning” (MKL) allows the practitioner to optimize over linear combinations of kernels. By enforcing sparse coefficients, it also generalizes feature selection to kernel selection. We propose MKL for joint feature maps. This provides a convenient and principled way for MKL with multiclass problems. In addition, we can exploit the joint feature map to learn kernels on output spaces. We show the equivalence of several different primal formulations including different regularizers. We present several optimization methods, and compare a convex quadratically constrained quadratic program (QCQP) and two semi-infinite linear programs (SILPs) on toy data, showing that the SILPs are faster than the QCQP. We then demonstrate the utility of our method by applying the SILP to three real world datasets. 1.	SVM HeaderParse 0.2
VENUE	In ICML. ACM	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	2007	INFERENCE
CITATIONS	21 found	ParsCit 1.0