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A nonlinear classifier can classify linearly inseparable samples, nevertheless the time complexity of the nonlinear classifier will be increased when processing linearly separable samples.
And it's a nonlinear classifier.
For some problems, there exists a nonlinear classifier with zero classification error, but no such linear classifier.
"We show that this quantum model of a perceptron can be used as an elementary nonlinear classifier of simple patterns," say Tacchino and co.
This article presents an application of fuzzy nonlinear classifier based on fuzzy If-Then rules to speaker verification and identification.
To cope with the high intra-class variability, we adopted a nonlinear classifier: the Support Vector Machines (SVM).
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Table 2 shows the average accuracies of applying the linear and nonlinear classifiers to classify all of the five EEG datasetes.
Nonlinear classifiers are more powerful than linear classifiers.
Does that mean that we should always use nonlinear classifiers for optimal effectiveness in statistical text classification?
Throughout this section, we use linear and nonlinear classifiers as prototypical examples of ''less powerful" and 'moree powerful" learning, respectively.
The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting.
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CEO of Professional Science Editing for Scientists @ prosciediting.com