This paper considers the epistemic justification for a simplicity preference in inductive inference that may be obtained from the machine learning framework of statistical learning theory. Uniting elements from both earlier arguments suggesting and rejecting such a justification, the paper spells out a qualified means-ends and model-relative justificatory argument, built on statistical learning theory's central mathematical learning guarantee for the method of empirical risk minimization.

翻译：本文探讨了从统计学习理论的机器学习框架中可能获得的对归纳推理中简单性偏好的认识论辩护。通过整合早期既支持又反对此种辩护的论证要素，本文阐明了一种基于统计学习理论核心数学学习保证——针对经验风险最小化方法——的有条件的"手段-目的"及模型相对性辩护论证。