Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods (Advances In Computer Vision And Pattern Recog,Used

Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods (Advances In Computer Vision And Pattern Recog,Used

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This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in realworld settings. The broad coverage examines such cuttingedge topics as the use of information theory to enhance unsupervised learning in treebased methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernelbased methods, and treebased methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.

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This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).

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