24 research outputs found
The Rate of Convergence of AdaBoost
The AdaBoost algorithm was designed to combine many "weak" hypotheses that
perform slightly better than random guessing into a "strong" hypothesis that
has very low error. We study the rate at which AdaBoost iteratively converges
to the minimum of the "exponential loss." Unlike previous work, our proofs do
not require a weak-learning assumption, nor do they require that minimizers of
the exponential loss are finite. Our first result shows that at iteration ,
the exponential loss of AdaBoost's computed parameter vector will be at most
more than that of any parameter vector of -norm bounded by
in a number of rounds that is at most a polynomial in and .
We also provide lower bounds showing that a polynomial dependence on these
parameters is necessary. Our second result is that within
iterations, AdaBoost achieves a value of the exponential loss that is at most
more than the best possible value, where depends on the dataset.
We show that this dependence of the rate on is optimal up to
constant factors, i.e., at least rounds are necessary to
achieve within of the optimal exponential loss.Comment: A preliminary version will appear in COLT 201
The Rate of Convergence of AdaBoost
The AdaBoost algorithm was designed to combine many “weak” hypotheses that perform slightly better than random guessing into a “strong” hypothesis that has very low error. We study the rate at which AdaBoost iteratively converges to the minimum of the “exponential loss”. Unlike previous work, our proofs do not require a weak-learning assumption, nor do they require that minimizers of the exponential loss are finite. Our first result shows that the exponential loss of AdaBoost's computed parameter vector will be at most ε more than that of any parameter vector of ℓ[subscript 1]-norm bounded by B in a number of rounds that is at most a polynomial in B and 1/ε. We also provide lower bounds showing that a polynomial dependence is necessary. Our second result is that within C/ε iterations, AdaBoost achieves a value of the exponential loss that is at most ε more than the best possible value, where C depends on the data set. We show that this dependence of the rate on ε is optimal up to constant factors, that is, at least Ω(1/ε) rounds are necessary to achieve within ε of the optimal exponential loss.National Science Foundation (U.S.) (Grant IIS-1016029)National Science Foundation (U.S.) (Grant IIS-1053407
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