Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Hardcover, 2013 ed.)

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 kernel-based methods, and tree-based 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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Advances in Computer Vision and Pattern Recognition
Release date:	June 2013
First published:	2013
Authors:	Chris Aldrich • Lidia Auret
Dimensions:	235 x 155 x 21mm (L x W x T)
Format:	Hardcover
Pages:	374
Edition:	2013 ed.
ISBN-13:	978-1-4471-5184-5
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-4471-5184-4
Barcode:	9781447151845

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!