In addition, the distance to the hyperplane indicates the confidence of classification: the farther a point from the plane, the higher the confidence of a correct classification (see also the discussion of Platt SVM
Para la implementacion de SVMs
se deben considerar las caracteristicas de los datos a clasificar; en este trabajo se busca mostrar este aspecto variando el parametro r de las ecuaciones de Lorenz.
In order to identify automatically intervals with fossiliferous limestone and calcareous shale interbedded with limestone two sub-models based on SVMs
To generate the error curves, SVMs
with polynomial (quadratic) kernel, were employed as classifiers.
For classification purposes, we used as basis of comparison both linear and RBF SVMs
using PCA reduced features, and random forests (RF).
The key advantage of the SVMs
is structural risk minimisation over the empirical risk minimisation which checks overfitting during model development [29, 33].
More recently, Diederich published a book on techniques to extract symbolic rules from Support Vector Machines (SVMs
)  and Barakat and Bradley reviewed a number of rule extraction techniques applied to SVMs
8 Actual 1 2 3 Predicted 3 0 0 1 0 0 0 2 0 1 8 3 Average Table 3: Performance of the proposed approach, the empirical formula, and the two-class SVMs
used to determine the squeezing.
Here, a cross-validated SVM
classifier was optimized using Bayesian optimization.
While several learning techniques focus on minimizing the error rate generated by the model based on the training samples, SVMs
attempt to minimize the so-called structural risk.