用皮尔士逻辑统一形式学习、统计推断与监督学习

Unified Inductive Logic: From Formal Learning to Statistical Inference to Supervised Learning

• 以皮尔士逻辑替代卡纳普范式，构建统一框架
• 三大领域共用的非演绎推理标准可被同一原则证明
• 适合对逻辑基础与机器学习交叉感兴趣的读者

传统归纳逻辑以卡纳普理论为核心，本文提出一种皮尔士式的替代方案，并以此统一形式学习理论、统计推断与监督学习的大部分内容。这些领域各自采用的关键推理评价标准，实际上均可由一个统一原则加以证明。该框架揭示了跨学科推理机制的深层一致性。

原文摘要 · Abstract (English)

While the traditional conception of inductive logic is Carnapian, I develop a Peircean alternative and use it to unify formal learning theory, statistics, and a significant part of machine learning: supervised learning. Some crucial standards for evaluating non-deductive inferences have been assumed separately in those areas, but can actually be justified by a unifying principle.

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