Convolutional Model Trees
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908789823766528 |
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| author | Armstrong, William Ward Li, Hongyi Xu, Jun |
| author_facet | Armstrong, William Ward Li, Hongyi Xu, Jun |
| contents | A method for creating a forest of model trees to fit samples of a function defined on images is described in several steps: down-sampling the images, determining a tree's hyperplanes, applying convolutions to the hyperplanes to handle small distortions of training images, and creating forests of model trees to increase accuracy and achieve a smooth fit. A 1-to-1 correspondence among pixels of images, coefficients of hyperplanes and coefficients of leaf functions offers the possibility of dealing with larger distortions such as arbitrary rotations or changes of perspective. A theoretical method for smoothing forest outputs to produce a continuously differentiable approximation is described. Within that framework, a training procedure is proved to converge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12725 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convolutional Model Trees Armstrong, William Ward Li, Hongyi Xu, Jun Machine Learning 41.A45 I.2.6; I.2.9; I.2.10 A method for creating a forest of model trees to fit samples of a function defined on images is described in several steps: down-sampling the images, determining a tree's hyperplanes, applying convolutions to the hyperplanes to handle small distortions of training images, and creating forests of model trees to increase accuracy and achieve a smooth fit. A 1-to-1 correspondence among pixels of images, coefficients of hyperplanes and coefficients of leaf functions offers the possibility of dealing with larger distortions such as arbitrary rotations or changes of perspective. A theoretical method for smoothing forest outputs to produce a continuously differentiable approximation is described. Within that framework, a training procedure is proved to converge. |
| title | Convolutional Model Trees |
| topic | Machine Learning 41.A45 I.2.6; I.2.9; I.2.10 |
| url | https://arxiv.org/abs/2511.12725 |