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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Aldrich, Chris; Auret, Lidia
€ 208.25
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Description for Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Hardback. This book describes the latest developments in nonlinear methods and their application in fault diagnosis. It details advances in machine learning theory and contains numerous case studies with real-world data from industry. Series: Advances in Computer Vision and Pattern Recognition. Num Pages: 393 pages, 57 black & white illustrations, 151 colour illustrations, 56 black & white tables, biogra. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 243 x 157 x 21. Weight in Grams: 694.
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 real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based 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 ... Read more
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 real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based 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 ... Read more
Product Details
Format
Hardback
Publication date
2013
Publisher
Springer London Ltd United Kingdom
Number of pages
393
Condition
New
Series
Advances in Computer Vision and Pattern Recognition
Number of Pages
374
Place of Publication
England, United Kingdom
ISBN
9781447151845
SKU
V9781447151845
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
From the reviews: “The text elaborates a range of classifiers used for supervised and unsupervised machine learning methods, for different types of processes. … The rich examples of various industrial processes and the illustration of subsequent simulation results qualify the work as a reference textbook for graduate studies in machine learning.” (C. K. Raju, Computing Reviews, October, 2013)