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.
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.
Afterwards, a more complicated dataset of two classes was tested.
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.
The last dataset tested with SVM is illustrated in Figure 5.
By applying again SVM with Gaussian kernel, the decision boundary is shown in Figure 6.
A more detailed description of this project’s implentation in Matlab can be seen in this github repository: Link to Github repository