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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.07641 |
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| _version_ | 1866912268555386880 |
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| author | Melton, Niklas M. da Silva, Leonardo Enzo Brito Petrenko, Sasha Wunsch II, Donald. C. |
| author_facet | Melton, Niklas M. da Silva, Leonardo Enzo Brito Petrenko, Sasha Wunsch II, Donald. C. |
| contents | This paper presents Deep ARTMAP, a novel extension of the ARTMAP architecture that generalizes the self-consistent modular ART (SMART) architecture to enable hierarchical learning (supervised and unsupervised) across arbitrary transformations of data. The Deep ARTMAP framework operates as a divisive clustering mechanism, supporting an arbitrary number of modules with customizable granularity within each module. Inter-ART modules regulate the clustering at each layer, permitting unsupervised learning while enforcing a one-to-many mapping from clusters in one layer to the next. While Deep ARTMAP reduces to both ARTMAP and SMART in particular configurations, it offers significantly enhanced flexibility, accommodating a broader range of data transformations and learning modalities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07641 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep ARTMAP: Generalized Hierarchical Learning with Adaptive Resonance Theory Melton, Niklas M. da Silva, Leonardo Enzo Brito Petrenko, Sasha Wunsch II, Donald. C. Machine Learning Artificial Intelligence Neural and Evolutionary Computing This paper presents Deep ARTMAP, a novel extension of the ARTMAP architecture that generalizes the self-consistent modular ART (SMART) architecture to enable hierarchical learning (supervised and unsupervised) across arbitrary transformations of data. The Deep ARTMAP framework operates as a divisive clustering mechanism, supporting an arbitrary number of modules with customizable granularity within each module. Inter-ART modules regulate the clustering at each layer, permitting unsupervised learning while enforcing a one-to-many mapping from clusters in one layer to the next. While Deep ARTMAP reduces to both ARTMAP and SMART in particular configurations, it offers significantly enhanced flexibility, accommodating a broader range of data transformations and learning modalities. |
| title | Deep ARTMAP: Generalized Hierarchical Learning with Adaptive Resonance Theory |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2503.07641 |