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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/79643

    Title: 影響蝴蝶辨識模型能力之因素探討與比較;Discussion and comparison of factors affecting the ability of butterfly identification model
    Authors: 鄭皓友;Cheng, Hao-Yu
    Contributors: 數學系
    Keywords: 深度學習;影像辨識;卷積神經網路;dropout;池化層;優化演算法;Deep Learning;Image recognition;Convolutional neural network;dropout;pooling layer;optimization algorithm
    Date: 2019-01-21
    Issue Date: 2019-04-02 15:10:02 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 影像辨識是人工智慧中的熱門領域,可以應用在許多地方,例如手寫數字辨識、車牌辨識、人臉辨識、物體辨識等等。使用深度學習的方法可以有效的提取特徵且降低人力成本,但要創造出一個好的分類模型需要考量很多因素。例如:合適的模型架構,合適的優化方法、合適的參數設定等等。
    ;Image recognition is popular in artificial intelligence and can be applied to many fields, such as handwritten digit recognition, license plate recognition, face recognition, object recognition and so on. Using deep learning methods can effectively extract features and reduce costs. But, creating a good classification model requires consideration of many factors. For example: the appropriate model architecture, the appropriate optimization method, the appropriate parameter settings, and so on.
    The butterfly images of this experiment are taken from ImageNet, and the butterfly identification models are constructed by the convolutional neural network. Several factors that may affect the butterfly identification model are selected as the objects of discussion and comparison. It is observed from the experimental results that the size of the dropout ratio, the size and placement of the pooling layer, the different optimization algorithms and the different layers of convolution layers all affect the ability of the butterfly identification model. Therefore, when constructing the model, these factors must be carefully chosen, and their influence on the model cannot be ignored.
    Appears in Collections:[數學研究所] 博碩士論文

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