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Found 11 papers, 4 papers with code
Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory
In the clustering domain, various algorithms with a federated learning framework (i. e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy.
A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning
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 clustering performance.
Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization
Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimization problems (MaOPs).
Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological Clustering
This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm.
Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning
In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm.
Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation
To that effect, this paper considers an existing evolutionary reinforcement learning framework which exploits multi-objective optimization as a way to obtain policies that succeed at behavior-related tasks as well as completing the main goal.
Multi-label Classification via Adaptive Resonance Theory-based Clustering
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 approach for label probability computation.
Identifying Properties of Real-World Optimisation Problems through a Questionnaire
Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences.
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems
This work aims to identify properties of real-world problems through a questionnaire on real-world single-, multi-, and many-objective optimization problems.
A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go
This paper applies a genetic algorithm and fuzzy markup language to construct a human and smart machine cooperative learning system on game of Go.
FML-based Prediction Agent and Its Application to Game of Go
In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application.
