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A gating function formulates the effectiveness of the k th kernel in classification of sample x i.
In the whole process of the unsupervised classification, there is no classification of sample participation, and the common used algorithms are K-Means and ISODATA.
Classification of sample i to the risk groups is then based on the i th leave-one-out risk index.
Classification of sample outcome using this vector was performed using the Random Forest classifier [ 50] in the WEKA toolbox [ 51].
A common example is the classification of sample types (e.g. tumor and healthy) with gene expression data [ 37, 72, 73].
Participants completed a personal details questionnaire, comprising demographic and education-related details enabling classification of sample characteristics by group, site and entire study.
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It is mainly based on the techniques of sector division and correct classification of samples.
Multivariate methods were used for patterns recognition and the classification of samples.
Table 3 shows the classification of samples according to standards specified for different water quality parameters.
Table 5 represents classification of samples according to standards specified for different water quality parameters.
These results confirmed that the synchronous fluorescence spectra provide information related to the molecular structure of meat, allowing classification of samples as a function of cooking time & temperature.
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