Implementing an SVM with data augmentation in Python
In this section, we will provide a step-by-step guide to implement an SVM with data augmentation in Python using the CIFAR-10 dataset. We will start by introducing the CIFAR-10 dataset and then move on to loading the dataset in Python. We will then preprocess the data for SVM training and implement an SVM with the default hyperparameters and dataset. Next, we train and evaluate the performance of the SVM with an augmented dataset, showing that the performance of the SVM improves on the augmented dataset.
Introducing the CIFAR-10 dataset
The CIFAR-10 dataset is a commonly used image classification dataset that consists of 60,000 32x32 color images in 10 classes. The classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. The dataset is divided into 50,000 training images and 10,000 testing images. The dataset is preprocessed in a way that the training set and test set have an equal number of images...
