Feature Extraction Using Convolutional Neural Networks Followed by SVM Classification

In this approach, we first utilize convolutional neural networks (CNNs) to extract features from the data, followed by classification using Support Vector Machines (SVM). This hybrid method leverages CNN's powerful feature extraction capabilities through convolutional and pooling layers, which automatically learn hierarchical representations from raw input data. The extracted features are then fed into an SVM classifier, which constructs optimal hyperplanes for separation using kernel functions like RBF or linear kernels. This combination allows for more effective data processing and analysis, leading to improved classification accuracy.

Furthermore, we can integrate additional machine learning algorithms to further optimize the classification process, such as Random Forest for ensemble learning or Logistic Regression for probabilistic classification. By implementing these methods through frameworks like TensorFlow/Keras for CNN implementation and scikit-learn for SVM and other classifiers, we can more comprehensively utilize data characteristics while enhancing classification accuracy and stability through techniques like cross-validation and hyperparameter tuning.