**Table of Contents**

- Using a perceptron for solving a linearly separable binary classification problem
- Using a feed-forward neural network for solving a classification problem
- The influence of the number of hidden neurons to the performance of the feed-forward neural network
- Using a linear SVM for solving a linearly separable binary classification problem
- The influence of the parameter C to the performance of the linear SVM (1)
- The influence of the parameter C to the performance of the linear SVM (2)
- The influence of the parameter C to the performance of the linear SVM (3)
- The influence of the number of training examples to the performance of the linear SVM
- Solving the two spirals problem by a nonlinear SVM
- The influence of the kernel width parameter to the performance of the RBF kernel SVM
- Search for optimal parameter values of the RBF kernel SVM
- Using an SVM for solving a multi-class classification problem
- Using an SVM for solving a regression problem

In this experiment a perceptron is trained on a linearly separable
two-dimensional data set consisting of two classes, that is a subset
of the *Wine* data set. The classification accuracy
of the perceptron is determined on the data set.

**Figure 8.1. A linearly separable subset of the Wine
data set [UCI MLR] used in the experiment (2 of
the total of 3 classes and 2 of the total of 13 attributes was selected).**

The second figure shows that the perceptron perfectly classifies all training examples.