svm

tracting features (e.g., HOG, SURF, or raw pixel values), 3) Splitting data into training and testing sets, 4) Training an SVM model using the training features and labels, 5) Evaluating the model on the test set, and 6

or classification tasks. Normalization/Standardization: Scale features to ensure uniformity, which improves SVM performance. Handling Missing Data: Address gaps in data to prevent biases or errors during training. 2. Assigning Fuzzy Membership