Statistical learning theory and Occam's razor: Regularization

2026-08-04

AI Summary

This study provides a rigorous methodological justification for the Occam’s razor principle—favoring simplicity—as embodied in regularization within machine learning, addressing its longstanding lack of theoretical grounding in inductive inference. Drawing on statistical learning theory and employing a means–ends analytical framework, the work demonstrates that balancing model fit against simplicity is essential for achieving reliable learning and “what you see is what you get” generalization guarantees. For the first time, regularization is established not merely as an engineering heuristic but as a fundamental mechanism ensuring the theoretical reliability of learning algorithms, from a non-pragmatic and non-ontological perspective. This advances a deeper understanding of the intrinsic relationship between model simplicity and generalization capacity.

Abstract

The principle of Occam's razor, which instructs us to prefer simplicity in inductive inference, has attracted much scrutiny both in the philosophy of science and in machine learning. In either field, however, a justification for the principle has been elusive. In this paper, building on an earlier "core argument," I spell out a justification from statistical learning theory for the procedure of regularization: for trading off fit for simplicity. The means-ends argument is that in order to profit from theoretical reliability and "what-you-see-is-what-you-get" guarantees, one must implement a certain preference for simplicity over fit. This is a genuine methodological justification, which neither collapses to a purely pragmatic principle that we prefer simplicity for its own sake, nor to an ontological assumption that the truth is simple.

Problem

• Occam's razor
• statistical learning theory
• regularization
• simplicity
• inductive inference

Innovation

• regularization
• Occam's razor
• statistical learning theory
• simplicity
• inductive inference