Support Vector Machines
- The goal of this project is to create a model that classifies various types of data. Support Vector Machines consist a well-established technique of machine learning applied in classification tasks.
The first dataset can be seen in Figure 1.
Figure 1: Input dataset 1
Data of two classes can be seperated by a linear decision boundary. Thus, by aplying SVM, data points are linearly seperated as you can see in Figure 2.
Figure 2: Decision boundary
Afterwards, a more complicated dataset of two classes was tested.
Figure 3: Input dataset 2
In this case, a non-linear decision boundary is required in order to classify data points correctly. Thus, a more complex form of SVM was used, called Gaussian kernel. The generated decision boundary is shown in Figure 4.
Figure 4: Decision boundary
The last dataset tested with SVM is illustrated in Figure 5.
Figure 5: Input dataset 3
By applying again SVM with Gaussian kernel, the decision boundary is shown in Figure 6.
Figure 6: Decision boundary
A more detailed description of this project’s implentation in Matlab can be seen in this github repository: Link to Github repository