提出可处理任意数据变换的分层学习模型，支持有监督与无监督训练。
Deep ARTMAP: Generalized Hierarchical Learning with Adaptive Resonance Theory
- 基于自适应共振理论构建分层聚类架构，模块间动态调节聚类结果。
- 支持任意数量模块及可调粒度，实现跨层一对多映射关系。
- 兼容传统ARTMAP和SMART模型，适用于复杂数据变换场景。
本文提出Deep ARTMAP，一种将自洽模块化ART（SMART）架构推广至任意数据变换下的分层学习框架。该模型作为分治式聚类机制，支持任意数量模块及各模块内可定制粒度。模块间通过交互调控各层聚类，既支持无监督学习，又强制实现上层聚类到下层的“一对多”映射。尽管Deep ARTMAP在特定配置下退化为ARTMAP和SMART，但其具备显著更高的灵活性，可处理更广泛的数据变换与学习模式。
原文摘要 · Abstract (English)
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.
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