Скачать или смотреть DEVELOP A SMALL IMAGE CLASSIFICATION ENSEMBLE COMBINING CNN AND SVM(HYBRID MODEL)

In this video, we’ll explore how to build a hybrid image classification model that combines the power of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) for better accuracy and performance. We’ll start by collecting and preprocessing an image dataset to ensure it’s clean and ready for training. Next, we’ll use a CNN to automatically extract deep visual features from the images, capturing essential patterns like edges, textures, and shapes. Instead of using the CNN’s fully connected layer for classification, we’ll feed these extracted features into an SVM classifier, which excels at finding optimal decision boundaries. Finally, we’ll combine the predictions from both CNN and SVM, evaluate the model using accuracy and other performance metrics, and compare how this hybrid approach outperforms standalone models. This project demonstrates the best of both worlds — the feature-learning ability of deep learning and the precision of classical machine learning.