9 citations · 23 across the 7 of their papers we have counts for
5 papers · 1 filter
An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter
Naoki Masuyama, Yuichiro Toda, Yusuke Nojima +1
Clustering in stationary and nonstationary settings, where data distributions remain static or evolve over time, requires models that can adapt to distributional shifts while prese…
Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory
Naoki Masuyama, Yusuke Nojima, Yuichiro Toda +3
With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with…
Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological Clustering
Naoki Masuyama, Yusuke Nojima, Farhan Dawood +1
This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering alg…
Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning
Naoki Masuyama, Narito Amako, Yuna Yamada +2
Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general…
Multi-label Classification via Adaptive Resonance Theory-based Clustering
Naoki Masuyama, Yusuke Nojima, Chu Kiong Loo +1
This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian…