A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Competitive learning algorithms for vector quantization
Neural Networks
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Optimization of clustering criteria by reformulation
IEEE Transactions on Fuzzy Systems
Fast online graph clustering via Erdős-Rényi mixture
Pattern Recognition
Fuzzy Q-Map Algorithm for Reinforcement Learning
Computational Intelligence and Security
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A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) algorithm is proposed that constitutes a reinforcement-based adaptation of learning vector quantization (LVQ) with enhanced clustering capabilities. In addition, we suggest extensions of RGCL and LVQ that are characterized by the property of sustained exploration and significantly improve the performance of those algorithms, as indicated by experimental tests on well-known data sets.