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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.20274 |
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| _version_ | 1866918400330039296 |
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| author | Sterkenburg, Tom F. |
| author_facet | Sterkenburg, Tom F. |
| contents | This chapter discusses the Solomonoff approach to universal prediction. The crucial ingredient in the approach is the notion of computability, and I present the main idea as an attempt to meet two plausible computability desiderata for a universal predictor. This attempt is unsuccessful, which is shown by a generalization of a diagonalization argument due to Putnam. I then critically discuss purported gains of the approach, in particular it providing a foundation for the methodological principle of Occam's razor, and it serving as a theoretical ideal for the development of machine learning methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20274 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Solomonoff induction Sterkenburg, Tom F. Formal Languages and Automata Theory Machine Learning This chapter discusses the Solomonoff approach to universal prediction. The crucial ingredient in the approach is the notion of computability, and I present the main idea as an attempt to meet two plausible computability desiderata for a universal predictor. This attempt is unsuccessful, which is shown by a generalization of a diagonalization argument due to Putnam. I then critically discuss purported gains of the approach, in particular it providing a foundation for the methodological principle of Occam's razor, and it serving as a theoretical ideal for the development of machine learning methods. |
| title | Solomonoff induction |
| topic | Formal Languages and Automata Theory Machine Learning |
| url | https://arxiv.org/abs/2603.20274 |