English(EN) Learning DNF through Generalized Fourier Representations

新的傅里叶表示支持在复杂分布下进行DNF学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-03 04:00

研究人员开发了一种广义傅里叶表示，以应对在非乘积分布下学习析取范式（DNF）的挑战。这种新方法将任何分布表示为贝叶斯网络，从而能够改编标准的基于傅里叶的学习技术。该工作证明了在某些贝叶斯网络下，合取的谱范数保持有界，推广了先前的发现，并确立了在这些分布下DNF和决策树的可学习性。 AI

影响引入了一种新颖的理论框架来学习复杂数据分布，有可能提升机器学习算法的能力。

排序理由这是一篇详细介绍一种新机器学习理论方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Mohsen Heidari, Roni Khardon · 2026-06-03 04:00

通过广义傅里叶表示学习DNF

arXiv:2506.01075v2 Announce Type: replace-cross Abstract: The Boolean Fourier representation has been widely used in learning theory, particularly for learning Disjunctive Normal Form (DNF) under uniform and product distributions. Extending these results to non-product distributi…