Soft Independent Modelling of Class Analogies
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High Quality Content by WIKIPEDIA articles! Soft independent modelling by class analogy (SIMCA) is a statistical method for supervised classification of data. The method requires a training data set consisting of samples (or objects) with a set of attributes and their class membership. The term soft refers to the fact the classifier can identify samples as belonging to multiple classes and not necessarily producing a classification of samples into non-overlapping classes. In order to build the classification models, the samples belonging to each class need to be analysed using principal compon...
High Quality Content by WIKIPEDIA articles! Soft independent modelling by class analogy (SIMCA) is a statistical method for supervised classification of data. The method requires a training data set consisting of samples (or objects) with a set of attributes and their class membership. The term soft refers to the fact the classifier can identify samples as belonging to multiple classes and not necessarily producing a classification of samples into non-overlapping classes. In order to build the classification models, the samples belonging to each class need to be analysed using principal components analysis (PCA); only the significant components are retained.
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