Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
Swarm intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hebbian Learning And Negative Feedback Networks (Advanced Information and Knowledge Processing)
Hebbian Learning And Negative Feedback Networks (Advanced Information and Knowledge Processing)
Particle Swarm Optimization of Feed-Forward Neural Networks with Weight Decay
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Two topographic maps for data visualisation
Data Mining and Knowledge Discovery
Non-Standard Parameter Adaptation for Exploratory Data Analysis
Non-Standard Parameter Adaptation for Exploratory Data Analysis
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We discuss extensions of particle swarm based optimization (PSO) algorithms in the context of exploratory data analysis. In particular, we apply these extensions to principal component analysis, exploratory projection pursuit and topology preserving mappings. Our extensions include combining PSO algorithms with stochastic sampling and a form of reinforcement learning known as Q-learning. We illustrate on a variety of artificial data sets and show that our new results are better than previous results on such data sets.