Project details for mcmkl

Screenshot mcmkl 0.1

by ong - May 15, 2008, 15:30:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Description:

We provide some preliminary code for multiclass multiple kernel learning in Matlab using CPLEX as a base solver.

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.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Linux, Macos
Data Formats: None
Tags: Mkl, Kernel Methods, Multi Class, Convex Optimization
Archive: download here

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