Unified Inductive Logic: From Formal Learning to Statistical Inference to Supervised Learning
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916628408565760 |
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| author | Lin, Hanti |
| author_facet | Lin, Hanti |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unified Inductive Logic: From Formal Learning to Statistical Inference to Supervised Learning Lin, Hanti Other Statistics Machine Learning Methodology 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. |
| title | Unified Inductive Logic: From Formal Learning to Statistical Inference to Supervised Learning |
| topic | Other Statistics Machine Learning Methodology |
| url | https://arxiv.org/abs/2412.02969 |