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Next best methods are supervised learners KNN and Gaussian NB.
The majority of these methods are supervised, requiring manual labeling of video clips.
The methods are supervised, requiring fixed basis functions trained using the instrument modeling procedure in Section 2.4.
SVM Methods are supervised learning models with associated learning algorithms that analyse data and recognize patterns and are used for classification and regression analysis.
However, because these methods are supervised classification methods, they require large amounts of transcriptionally profiled tumors with identified origin upon which to train.
An important distinction that can be made is whether methods are supervised (i.e. they depend on annotated training data), or unsupervised.
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Unlike the Eigenfaces method, which is unsupervised, the Fisherfaces method is supervised.
First, unlike the unsupervised methods we consider, their method is supervised.
In addition, GNDE and KGNDE are supervised methods.
GPR models are supervised learning methods based on a generalized linear regression that locally estimates forecasts by the combination of values in a kernel (Rasmussen 1996).
Because both tree-based algorithms and SVM are supervised machine learning methods, an unsupervised clustering method was used to independently optimize the classifier genes.
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