Virtual Sensors for Semiconductor Manufacturing: A Nonparametric Approach - Exploiting Information Theoretic Learning and Kernel Machines

DOI10.1007/978-3-642-31353-0_13zbMath1308.93176OpenAlexW168210850MaRDI QIDQ5245446

Cristina de Luca, De Nicolao, Giuseppe, Simone Pampuri, Andrea Schirru

Publication date: 8 April 2015

Published in: Informatics in Control, Automation and Robotics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-31353-0_13

zbMATH Keywords

entropykernel methodssemiconductorsmachine learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Production models (90B30) Application models in control theory (93C95) Estimation and detection in stochastic control theory (93E10) Stochastic systems in control theory (general) (93E03)

Cites Work

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• Fault prognostics using dynamic wavelet neural networks
• Information Theoretic Learning
• On Estimation of a Probability Density Function and Mode
• Learning from examples with information theoretic criteria

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