Web: http://arxiv.org/abs/2201.10713

Jan. 27, 2022, 2:10 a.m. | Naoki Masuyama, Narito Amako, Yuna Yamada, Yusuke Nojima, Hisao Ishibuchi

cs.LG updates on arXiv.org arxiv.org

Thanks to an ability for handling the plasticity-stability dilemma, Adaptive
Resonance Theory (ART) is considered as an effective approach for realizing
continual learning. In general, however, the clustering performance of
ART-based algorithms strongly depends on a similarity threshold, i.e., a
vigilance parameter, which is data-dependent and specified by hand. This paper
proposes an ART-based topological clustering algorithm with a mechanism that
automatically estimates a similarity threshold from a distribution of data
points. In addition, for the improving information extraction performance, …

arxiv clustering learning theory

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