26 citations · 61 across the 9 of their papers we have counts for
9 papers
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…
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
Naoki Masuyama, Takanori Takebayashi, Yusuke Nojima +3
In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on cluster…
Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization
Takato Kinoshita, Naoki Masuyama, Yiping Liu +2
Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimizat…
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…
Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation
Victor Villin, Naoki Masuyama, Yusuke Nojima
Generating various strategies for a given task is challenging. However, it has already proven to bring many assets to the main learning process, such as improved behavior explorati…
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…