This book implemented six different algorithms to classify images with prediction accuracy as the primary criterion and time consumption as the secondary one. The accuracies varied between about 30% and 90%, while the time consumptions varied from several seconds to more than one hour. Considering both criteria, the Pre-Trained AlexNet Features Representation plus a Classifier, such as the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM), was concluded as the best algorithm.
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This book implemented six different algorithms to classify images with prediction accuracy as the primary criterion and time consumption as the secondary one. The accuracies varied between about 30% and 90%, while the time consumptions varied from several seconds to more than one hour. Considering both criteria, the Pre-Trained AlexNet Features Representation plus a Classifier, such as the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM), was concluded as the best algorithm.
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