A meta-cognitive learning algorithm for a fully complex-valued relaxation network

DOI10.1016/J.NEUNET.2012.02.015zbMath1254.68223OpenAlexW2030842930WikidataQ43548886 ScholiaQ43548886MaRDI QIDQ1929717

Yanyan Li

Publication date: 9 January 2013

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2012.02.015

zbMATH Keywords

complex-valued neural networksmulti-category classificationfully complex-valued relaxation networkmeta-cognitionself-regulated learning mechanism

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (2)

A meta-cognitive learning algorithm for a fully complex-valued relaxation network ⋮ Progressive learning: a deep learning framework for continual learning

Uses Software

• UCI-ml

Cites Work

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• Risk-sensitive loss functions for sparse multi-category classification problems
• Complex-valued radial basis function network. I: Network architecture and learning algorithms
• Fully complex multi-layer perceptron network for nonlinear signal processing
• Statistical behavior and consistency of classification methods based on convex risk minimization.
• A meta-cognitive learning algorithm for a fully complex-valued relaxation network
• Orthogonality of Decision Boundaries in Complex-Valued Neural Networks

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