Sentence examples similar to spectrum classification from inspiring English sources

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Results: In independent validation, sensitivity and specificity of 84%and88%8%, respectively, have been obtained purely on the basis of spectra classification employing a classifier designed specifically to provide robustness.

For comparison, the partial least squares-discriminant analysis (PLS-DA) based on full-spectrum classification was also used as the reference.

Many feature selection methods are employed to decrease dimensionalities, remove noise, and extract meaningful features before mass spectra classification.

If there exists a novel pattern recognition algorithm able to attain a 99.5% level accuracy in mass spectra classification for an input proteomic profile, then the profile can be viewed as a profile biomarker in disease diagnosis.

Extracting features from the TF spectrum for classification or clustering is a non-trivial task, and several approaches have been proposed in literature.

"As a longstanding intelligence professional, McConnell has an awareness across the full spectrum of classification, and sees it as his duty in public service to foster the right kind of discussion so the nation's leadership can debate and mitigate the risks," Fisher said in a statement.

However, all definitions are clinically similar, with any variation at the 'lowest risk' part of the spectrum of classification.

It contains functions for structural similarity searching, clustering of compound libraries with a wide spectrum of classification algorithms and various utilities for managing complex compound data.

To allow a comparison between using a PCA and using the original spectra, the classification algorithms were each carried out first with the original spectra, and subsequently with only a number of PC scores.

PCA reduces dataset dimensionality and facilitates establishing patterns and grouping of similar spectra for classification.

This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method.

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