Core Concepts
PMBO combines polynomial approximation with Bayesian optimization to outperform classic methods and provide robustness in low-dimensional optimization problems.
Stats
PMBOは古典的なベイジアン最適化を上回り、進化的アルゴリズムと同等の性能を提供します。
PMBOは低次元最適化問題において堅牢性を示し、ハイパーパラメータ設定や相関関数の選択に対して優れた性能を発揮します。
Quotes
"Remarkably, PMBO performs comparably with state-of-the-art evolutionary algorithms such as the Covariance Matrix Adaptation – Evolution Strategy (CMA-ES)."
"We propose a surrogate-model-based optimization algorithm that uses polynomial interpolation to approximate f."