Support vector machines — role?
Find optimal hyperplane with maximum margin.
Local features — purpose?
Capture details from image regions.
Neural network — basic structure?
Layers of neurons with weights, biases, activation functions.
Bag of visual words — process?
Clusters local features into codewords.
Neural network — training?
Adjust weights via backpropagation.
Activation functions — role?
Introduce non-linearity into neural networks.
Transfer learning — definition?
Using pre-trained models for new tasks.
Loss functions — purpose?
Quantify prediction error.
Class scores estimation — meaning?
Predict confidence for each class.
Testez vos connaissances avec un QCM de 9 questions sur Fundamentals of Image Classification and Neural Networks.
1. Who introduced the Support Vector Machine (SVM) model and in which year?
2. What is the primary purpose of class scores estimation in image classification?
Révisez le cours complet dans la fiche de révision de Fundamentals of Image Classification and Neural Networks.
Voir la fiche →Intelligence Artificielle
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