26 citations · 61 across the 9 of their papers we have counts for
6 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…
Integrating White and Black Box Techniques for Interpretable Machine Learning
Eric M. Vernon, Naoki Masuyama, Yusuke Nojima
In machine learning algorithm design, there exists a trade-off between the interpretability and performance of the algorithm. In general, algorithms which are simpler and easier fo…
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…