264 research outputs found
Parallel coordinate descent for the Adaboost problem
We design a randomised parallel version of Adaboost based on previous studies
on parallel coordinate descent. The algorithm uses the fact that the logarithm
of the exponential loss is a function with coordinate-wise Lipschitz continuous
gradient, in order to define the step lengths. We provide the proof of
convergence for this randomised Adaboost algorithm and a theoretical
parallelisation speedup factor. We finally provide numerical examples on
learning problems of various sizes that show that the algorithm is competitive
with concurrent approaches, especially for large scale problems.Comment: 7 pages, 3 figures, extended version of the paper presented to
ICMLA'1
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