| Summary: | 碩士 === 國立中央大學 === 數學系 === 107 === The goal of this thesis is to explore the training results of “K Nearest Neighbor”, “multilayer perceptual neural network” , “Support Vector Machine” and the classic model of Convolutional neural network: “LENET” and “ALEXNET” in image recognition.
The butterfly images in this experiment are from ImageNet which is the largest database of image recognition. First, we bring the training data into our models, and observe the difference between training time and training accuracy for each model, then compare the iterative results. Next,we give the reasons that affect the training results. Finally, we put the test set into the trained model for prediction.We observe the accuracy of the test set, and analyzed the factors affecting the prediction.
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