Chapter 8. Classification Methods 4

Neural Networks and Support Vector Machines

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

Using a perceptron for solving a linearly separable binary classification problem

Description

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.

Input

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).

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).

Output

Figure 8.2. The decision boundary of the perceptron.

The decision boundary of the perceptron.

Figure 8.3. The classification accuracy of the perceptron on the data set.

The classification accuracy of the perceptron on the data set.

Interpretation of the results

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

Video

Workflow

ann_exp1.rmp

Keywords

perceptron
supervised learning
classification

Operators

Apply Model
Filter Examples
Perceptron
Performance (Classification)
Read CSV
Remove Unused Values