Analysis and test of efficient methods for building recursive deterministic perceptron neural networks

DOI10.1016/j.neunet.2007.07.009zbMath1254.68200OpenAlexW2142668830WikidataQ50900990 ScholiaQ50900990MaRDI QIDQ1931966

Ralph Birkenhead, D. Elizondo, Éric D. Taillard, Patrick Luyima, Mario Góngora

Publication date: 17 January 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2007.07.009

zbMATH Keywords

incremental learningnetwork topologyconvergence timegeneralisationmodular learningbatch learningrecursive deterministic perceptronperformance sensitivity analysis

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (1)

Analysis and test of efficient methods for building recursive deterministic perceptron neural networks

Uses Software

• UCI-ml

Cites Work

• The densest hemisphere problem
• Growing methods for constructing Recursive Deterministic Perceptron neural networks and knowledge extraction
• Support-vector networks
• Analysis and test of efficient methods for building recursive deterministic perceptron neural networks
• Multisurface method of pattern separation for medical diagnosis applied to breast cytology.
• Metaheuristics for Hard Optimization
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