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The composition process stops when the obtained features satisfy the initial user requirement.
Then, we applied the above three types of kernel functions to the obtained features.
Training: obtaining features for the definition of the user behavior pattern; Recognition: matching observed features against user behavior pattern.
Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault pattern identifications.
It is hoped that through this selection the obtained features will be generic regarding the embedding rate.
The obtained features were then fed into a support vector machine (SVM) classifier to identify multiple damage states.
Thus, we conclude that our approach improves the quality of the obtained features and contributes enormously to the reduction of the number of features.
At the second layer of our feature hierarchy we apply Non-Negative Sparse Coding (NNSC) to obtain features spanning a larger frequency and time region.
The algorithm factorizes each network to obtain features using graph communicability, and then collapses the feature matrices to predict temporal links.
Social-based metrics are also designed to characterize emails as social features; the obtained features are treated as an input of machine learning-based classifiers for email classification.
In the context of finite element model updating, experimentally obtained features are used to improve the quality of an initial finite element model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com