Jiang et al., 2017 - Google Patents

Unsupervised deep learning for data-driven reliability and risk analysis of engineered systems

Jiang et al., 2017

Document ID
3704112746370956283
Author
Jiang P
Maghrebi M
Crosky A
Saydam S
Publication year
Publication venue
Handbook of neural computation

External Links

Snippet

Reliability and risk analysis concerns analyzing and predicting the state transition of engineered systems given historic information. With an increasing volume of available data, conventional machine learning algorithms may fail to capture hidden patterns behind the …
Continue reading at www.sciencedirect.com (other versions)

Classifications

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    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
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