Unsupervised learning in general connectionist systems
1a Laboratório de Mecatrónica, Departamento de Engenharia Electrotécnica e de Computadores , Instituto Superior Técnico , Av. Rovisco Pais, 1096 Lisbon Codex, Portugal.
Network (Bristol, England)
|February 27, 2018
Summary
This study unifies connectionist systems using adaptive node parameters in neural networks. Generalized Hebbian learning and competitive learning show improved convergence and robustness.
Area of Science:
- Computational neuroscience
- Machine learning
Background:
- Connectionist systems encompass diverse neural network architectures.
- Existing models often lack a unified framework for adaptive parameters.
Purpose of the Study:
- To generalize neural network learning schemes for networks with adaptive node parameters.
- To unify different connectionist systems under a common framework.
Main Methods:
- Generalization of Hebbian learning and competitive learning algorithms.
- Introduction of adaptive node parameters controlling output intensity.
Main Results:
- Generalized Hebbian learning shows improved convergence rates in principal component analysis.
- Competitive learning with node parameters enhances robustness or discrimination by modulating neuron dominance.
Conclusions:
- Adaptive node parameters provide a unified framework for connectionist systems.
- The generalized learning schemes offer performance benefits in specific machine learning tasks.
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