博碩士論文 106522101 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊工程學系zh_TW
DC.creator陳頌恩zh_TW
DC.creatorSung-En Chenen_US
dc.date.accessioned2019-7-29T07:39:07Z
dc.date.available2019-7-29T07:39:07Z
dc.date.issued2019
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=106522101
dc.contributor.department資訊工程學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract隨著科學技術的發展和進步,學習模式也在不斷發展。在問題驅動的學習中,學生通過回答問題來澄清和驗證他們學到的知識。如此眾多的問題需要良好的管理。良好的管理可以避免由於模糊表達而將具有相同知識集的學習材料定義到不同部分的情況。在篇論文中,我們提出了一個專注於國中小數學題目的階層式分類系統。我們測試了不同文件表示和分類器的幾種組合,其中包括扁平多標籤分類和知識點的階層式多標籤分類。針對包含文本和圖像的當前問題,我們還提出了文字與影像的預處理方法和組合文字影像特徵的CNN分類模型。實驗表明,我們的階層式分類可以勝過扁平多標籤分類方法。zh_TW
dc.description.abstractWith the development and progress of science and technology, the learning patterns also evolve. In Question-Driven learning, students clarify and validate what they learn by answering questions. Such a large number of questions needs good management. A well-performed management can avoid the situation that learning materials with the same knowledge set are defined into different sections due to ambiguous expressions. In this work, a hierarchical classification system that focuses on K-12 learning materials is proposed. We test several combination of document representation with flatten label and hierarchical label of the knowledge points. In response to a current question that contains text and image, we also introduce a multi-modal preprocessing approach and a combined feature CNN model. The experiment shows that our hierarchical classification can outperform flatten multi-label classification methods.en_US
DC.subject階層式分類zh_TW
DC.subject卷積神經網路zh_TW
DC.subject支援向量機zh_TW
DC.subject詞向量zh_TW
DC.subject問題驅動學習zh_TW
DC.subjectHierarchical Classificationen_US
DC.subjectConvolutional Neural Networken_US
DC.subjectSupport Vector Machineen_US
DC.subjectWord2Vecen_US
DC.subjectQuestion-Driven Learningen_US
DC.title中小學數學題目之階層式跨知識點分類系統zh_TW
dc.language.isozh-TWzh-TW
DC.titleA hierarchical Multi-label Classification System of K-12 Cross-Knowledge Points Math Questionen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明