## Chapter 19. Classification Methods 4

*Neural networks and support vector machines*

## Solution of a linearly separable binary classification task by ANN and SVM

In this experiment, we teach a perceptron as a simple artificial neural network (ANN) and a support vector machine (
SVM) on a linearly separable two dimensional dataset with two classes. The dataset is a subset of the
*Wine* dataset. adatállomány egy részhalmaza. The classification accuracy of the classifiers
is determined on the dataset.

*Wine* [UCI MLR]

In order to apply the dataset for the experiment it have to go through a significant preprocessing.
This involves choosing `2`

attributes from the total `13`

by using the
`Drop`

operator, then deleting the second class of the `Class`

attribute
and changing the measure level to binary by `Metadata`

operator.

The goodness of fit of the perceptron model can be checked by the usual statistics (misclassification rate, the
number of incorrectly classified cases) and graphics (response and lift curve).

In the case of support vector machines (SVM), besides the above mentioned diagnostic tools, more details
are given about the support vectors, namely the number of support vectors, the size of the margin and
the list of support vectors.

### Interpretation of the results

The figures and statistics show that the perceptron and support vector machine classify perfectly all of the traning
cases as well.

perceptron |

supervised learning |

classification |

Data Source |

Drop |

Filter |

Graph Explore |

Metadata |

Neural Network |

Support Vector Machine |