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Support vector machine (SVM) is an effective method for supervised pattern recognition and has been widely used in the realm of bioinformatics [ 4, 14, 77, 78].
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In this respect, supervised student training and constructive feedback is urgently needed, as feedback represents one of the most effective methods for behaviour modification [ 71].
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Kernel deep stacking networks (KDSNs) are a novel method for supervised learning in biomedical research.
In this paper, we develop a novel feature transformation method for supervised linear dimensionality reduction.
A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed.
Results: We propose a new method for supervised classification of arrayCGH data.
In this article, we propose a new method for supervised classification, specifically designed for the processing of arrayCGH profiles.
There is certainly room for a more effective method.
We showed that supervised PCA was the most effective method.
Our experimental results show that supervised PCA is the most effective method in feature reduction.
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