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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/79649

    Title: 機器學習方法在蝴蝶辨識中之比較;The Comparison of Machine Learning Methods in Butterfly Identification
    Authors: 李毅信;LI, YI-SIN
    Contributors: 數學系
    Keywords: 影像辨識;K 最近鄰居分類法;多層感知神經網路;支持向量機;卷積神經網路
    Date: 2019-01-26
    Issue Date: 2019-04-02 15:10:14 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 本研究旨在探討「K 最近鄰居分類法(KNN) 」、「多層感知神經網路(MLP)」、 「支持向量機(SVM)」與卷積神經網路經典模型: 「LENET」與「ALEXNET」在圖像辨識上的訓練結果之差異。

    ;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.
    Appears in Collections:[數學研究所] 博碩士論文

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