English(EN) Learning Kernels by Alignment for Multiclass Bayes Classification

新框架通过对齐学习核函数用于多类贝叶斯分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新的多类贝叶斯分类框架，通过对齐学习核函数，超越了传统预选核函数的方法。该方法称为协同学习与推理（CLaI），训练一个嵌入以匹配由标签导出的目标核函数。该框架已扩展到包含学习到的马氏距离，与基于余弦相似度的变体相比，在CIFAR-10和PathMNIST等数据集上提高了准确性、收敛速度和校准误差。该方法统一了表示学习、核函数对齐和贝叶斯分类，有望用于各种分类任务。 AI

影响该框架统一了表示学习、核函数对齐和贝叶斯分类，有望提高多类分类性能。

排序理由该条目是一篇学术论文，详细介绍了一个新的机器学习框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Hollan Haule, Alfredo Gonzalez-Sulser, Javier Escudero · 2026-09-10 04:00

通过对齐学习多类贝叶斯分类的核

arXiv:2609.06474v1 Announce Type: new Abstract: Kernel methods separate data representation from decision-making, but typically require the kernel to be chosen in advance. We show that this kernel can instead be learned by alignment, and develop the resulting framework through th…

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通过对齐学习多类贝叶斯分类的核

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