SOURCE-LINKED INTELLIGENCE

Learning Kernels by Alignment for Multiclass Bayes Classification

arXiv · AI, language, vision and robotics · article · Sep 6, 2026 · UTC

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 the recently introduced Collaborative Learning and Inference (CLaI). We show that Collaborative Learning can be viewed as a kernel alignment process, in which an embedding is trained so that its induced similarity matches a label-derived target kernel. We also prove that Collaborative Inference is equivalent to kernel Bayes classification with Parzen-window density es

Read original source ↗ Open in workspace

recordType

paper

region

Global