Polyra Swarms: A Shape-Based Approach to Machine Learning
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913895689486336 |
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| author | Klüttermann, Simon Müller, Emmanuel |
| author_facet | Klüttermann, Simon Müller, Emmanuel |
| contents | We propose Polyra Swarms, a novel machine-learning approach that approximates shapes instead of functions. Our method enables general-purpose learning with very low bias. In particular, we show that depending on the task, Polyra Swarms can be preferable compared to neural networks, especially for tasks like anomaly detection. We further introduce an automated abstraction mechanism that simplifies the complexity of a Polyra Swarm significantly, enhancing both their generalization and transparency. Since Polyra Swarms operate on fundamentally different principles than neural networks, they open up new research directions with distinct strengths and limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13217 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Polyra Swarms: A Shape-Based Approach to Machine Learning Klüttermann, Simon Müller, Emmanuel Machine Learning Neural and Evolutionary Computing Symbolic Computation We propose Polyra Swarms, a novel machine-learning approach that approximates shapes instead of functions. Our method enables general-purpose learning with very low bias. In particular, we show that depending on the task, Polyra Swarms can be preferable compared to neural networks, especially for tasks like anomaly detection. We further introduce an automated abstraction mechanism that simplifies the complexity of a Polyra Swarm significantly, enhancing both their generalization and transparency. Since Polyra Swarms operate on fundamentally different principles than neural networks, they open up new research directions with distinct strengths and limitations. |
| title | Polyra Swarms: A Shape-Based Approach to Machine Learning |
| topic | Machine Learning Neural and Evolutionary Computing Symbolic Computation |
| url | https://arxiv.org/abs/2506.13217 |