However, some drawbacks must be underlined: in particular, they used a nonlinear classifier combining the seven features with support vector machine, Bayesian classifier, k-nearest neighbours, and logistic regression model, all approaches that determine a nonlinear manipulation of features; this latter is not easy to understand by radiologists with respect to a linear combination of features.
Best results were achieved using a nonlinear SVM classifier, but it is important to note that experiments with a simpler LDA-based classification scheme resulted in comparable performance under various spoofing attack scenarios.
Concentration response relationships were fit using a nonlinear regression.
IC50 values were calculated using a nonlinear regression curve fit.
Population pharmacodynamics was modeled using a nonlinear mixedeffect model.
When the CART model is not satisfactory, one can apply discriminant methods, such as DPLS and UVE-DPLS, or if necessary to use a nonlinear multivariate classifier like, e.g., support vector machines.
We aimed to improve the prediction performance: first, using new nonlinear features (detrended fluctuation analysis and sample entropy); second, using a multivariable nonlinear classifier (support vector machine); and finally, including additional physiological signals (stroke volume).
The use of transthoracic impedance (TI) was introduced in [16], comparing a set of parameters during rest by using a SVM nonlinear classifier (128 patients, 65 with positive), which yielded 94% sensitivity and 79% specificity.
A nonlinear classifier can classify linearly inseparable samples, nevertheless the time complexity of the nonlinear classifier will be increased when processing linearly separable samples.
To solve this problem, a linear classifier based on the rough k-means and a nonlinear classifier SVM are adopted.
