Sentence examples for binary classifiers have been from inspiring English sources

In most examples of cancer classification, binary classifiers have been used, with multi-class predictors only rarely applied.

A number of standard binary classifiers has been tested and their performances have been evaluated, namely Gaussian, Mixture of Gaussians, Neural Networks, K-Means, PCA, Parzen and Decision Trees [33, 34].

SVMs are a popular machine learning binary classifier that has been adapted to perform regression.

The models here are binary classifiers and the following measures have been utilised to assess the predictive performance of the models based upon true positive (TP), false positive (FP), true negative (TN) and false negative (FN) result classification.

[3/11/06] Classifier contest instructions have been posted.

We combine the results from the binary classifier with a voting scheme: every binary classifier has a vote, and a data point is classified into the class with the maximum number of votes.

At each run of LOOCV, the parameterSeventhe filtering pipeline have been set to maximize clustersdetection and minimize Figurepositives.

Each binary classifier has one vote and the final output is the class with the maximum number of votes.

In its basic form it is a linear, binary classifier, but it has been extended to non-linear, multiclass classification.

Traditionally, "one-to-one" or "one-to-the-rest" approaches have been applied to integrate combinations of unit binary classifiers.

Specifically, binary Support Vector Classifier (two-class SVM) and one-class Novelty Detection (SVND) have been only tested in some example images or in limited databases.