Web: http://arxiv.org/abs/2209.10831

Sept. 23, 2022, 1:11 a.m. | Ryotaro Mitsuboshi, Kohei Hatano, Eiji Takimoto

cs.LG updates on arXiv.org arxiv.org

Some boosting algorithms, such as LPBoost, ERLPBoost, and C-ERLPBoost, aim to
solve the soft margin optimization problem with the $\ell_1$-norm
regularization. LPBoost rapidly converges to an $\epsilon$-approximate solution
in practice, but it is known to take $\Omega(m)$ iterations in the worst case,
where $m$ is the sample size. On the other hand, ERLPBoost and C-ERLPBoost are
guaranteed to converge to an $\epsilon$-approximate solution in
$O(\frac{1}{\epsilon^2} \ln \frac{m}{\nu})$ iterations. However, the
computation per iteration is very high compared to LPBoost.


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