Practical machine learning is learning on small samples
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917883309719552 |
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| author | Sapir, Marina |
| author_facet | Sapir, Marina |
| contents | Based on limited observations, machine learning discerns a dependence which is expected to hold in the future. What makes it possible? Statistical learning theory imagines indefinitely increasing training sample to justify its approach. In reality, there is no infinite time or even infinite general population for learning. Here I argue that practical machine learning is based on an implicit assumption that underlying dependence is relatively ``smooth" : likely, there are no abrupt differences in feedback between cases with close data points. From this point of view learning shall involve selection of the hypothesis ``smoothly" approximating the training set. I formalize this as Practical learning paradigm. The paradigm includes terminology and rules for description of learners. Popular learners (local smoothing, k-NN, decision trees, Naive Bayes, SVM for classification and for regression) are shown here to be implementations of this paradigm. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_01836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Practical machine learning is learning on small samples Sapir, Marina Machine Learning Artificial Intelligence Based on limited observations, machine learning discerns a dependence which is expected to hold in the future. What makes it possible? Statistical learning theory imagines indefinitely increasing training sample to justify its approach. In reality, there is no infinite time or even infinite general population for learning. Here I argue that practical machine learning is based on an implicit assumption that underlying dependence is relatively ``smooth" : likely, there are no abrupt differences in feedback between cases with close data points. From this point of view learning shall involve selection of the hypothesis ``smoothly" approximating the training set. I formalize this as Practical learning paradigm. The paradigm includes terminology and rules for description of learners. Popular learners (local smoothing, k-NN, decision trees, Naive Bayes, SVM for classification and for regression) are shown here to be implementations of this paradigm. |
| title | Practical machine learning is learning on small samples |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.01836 |