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    Please use this identifier to cite or link to this item: http://chur.chu.edu.tw/handle/987654321/31956


    Title: 次分類神經網路在森林覆蓋類型預測的應用
    Authors: 葉怡成
    Yeh, I-Cheng
    Contributors: 資訊管理學系
    Information Management
    Keywords: 倒傳遞神經網路;森林地表覆蓋;分類;次分類
    back-propagation;cover;sub-category
    Date: 2008
    Issue Date: 2014-06-27 01:39:56 (UTC+8)
    Abstract: 雖然倒傳遞網路可以建構準確的分類模型，但無法發掘隱藏在分類中的「次分類」。為了改善倒傳遞網路不能產生次分類的缺點，在此提出次分類神經網路(Sub-Category Neural Network, SCNN)。為證明此一架構優於傳統的倒傳遞類神經網路（Back- Propagation Network, BPN），本文以二個人為的分類例題及一個真實的森林地表覆蓋類型(樹種)分類問題進行比較。由實驗結果歸納出下列結論：(1) SCNN的準確度與BPN相近。(2) SCNN可以將部份分類的「次分類」發掘出來。
    Although the back-propagation algorithm can build accurate classification models, it can not discover the sub-categories in the data set. To solve this problem, this study proposed a novel neural network model, Sub-Category Neural Network (SCNN). To prove
    Appears in Collections:[Department of Information Management] Journal Articles

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