Training support vector machines with augmented image data
Support Vector Machines (SVMs) are widely used in machine learning to solve classification problems. SVMs are known for their high accuracy and ability to handle complex datasets. One of the challenges in training SVMs is the availability of large and diverse datasets. In this section, we will discuss the importance of data augmentation in training SVMs for image classification problems. We will also provide Python code examples for each technique.
Figure 6.1 – SVM separates class A and class B with largest margin
SVMs are a type of supervised learning algorithm used for classification and regression analysis. SVMs can be used for outlier detection. SVMs were originally designed for classification tasks, but can also be adapted for anomaly or outlier detection as well.
The objective of SVMs is to find the hyperplane that maximizes the margin between two classes of data. The hyperplane...
