English(EN) Complementary Feature Domains: Information Preservation Does Not Imply Predictive-Contribution Preservation

新理论区分信息保持与预测贡献

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

一篇新论文介绍了互补特征域（CFD）理论，该理论将预测价值表征为一种与上下文相关的贡献系统。研究表明，保持香农信息不一定能保持这种贡献系统，因为可逆的表征变换会改变预测贡献。该论文用CFD贡献缺陷的形式化了这种变化，并表明对于有界Lipschitz效用，联盟效用变化受到行为距离的约束。一项使用心电图数据的实验说明了非线性重编码如何在保持信息的同时改变准确性，而精确的逆变换可以恢复准确性。 AI

影响引入了一个理论框架，该框架可能通过将信息内容与其预测效用分离开来，从而实现更强大的AI模型。

排序理由该集群包含一篇详细介绍机器学习新理论框架的预印本学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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arXiv cs.AI TIER_1 English(EN) · Timothy Oladunni, Farouk Ganiyu-Adewumi · 2026-10-07 04:00

互补特征域：信息保留不等于预测贡献保留

arXiv:2610.07565v1 Announce Type: cross Abstract: Complementary Feature Domains (CFD) theory characterizes predictive value as a context-indexed contribution system induced jointly by representations and their realization family. We show that Shannon-information preservation does…

本文如何被排名

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互补特征域：信息保留不等于预测贡献保留

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