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In establishing an OVR classifier for separating type A from others, the training data will be divided into two groups, one containing the A samples (8 samples) and another containing the remaining 72 samples (B to J).
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Using the error-correcting output codes (ECOC) to design binary classifiers (dichotomizers) for separating subsets of classes, the outputs of the dichotomizers are linear or nonlinear features that provide powerful separability in a new space.
During the classifier training, these parameters are tuned in order to define the optimum hyperplane for separating the data.
This is accomplished by first building a classifier for each separate source domain.
The model for the classifier is a separating hyperplane (1) f (x ) = w T x - b where x is a vector of gene expression levels.
The Ludowici LMPE Reflux Classifier is a new device designed for classifying and separating particles on the basis of size or density.
Messina is an algorithm for constructing classifiers capable of separating two sample groups (eg. cancer and normal tissue) on the basis of the expression level of a single gene.
The reflux classifier is a water based device designed for classifying and separating particles using inclined plates.
We propose a classifier that separates these four classes.
It is difficult to find a linear classifier to separate different classes in the dataset.
In the OvR scenario, k binary classifiers are trained, such that each classifier separates one class from all others.
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