博碩士論文 103522082 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:10 、訪客IP:3.94.21.209
姓名 何紹儀(Shao-Yi Ho)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 應用於中小學問題式學習結合卷積神經網路與支持向量機器之問題分類器
(A Self-relevant CNN-SVM Model for Problem Classification in K-12 Question-driven Learning)
相關論文
★ 具多重樹狀結構之可靠性群播傳輸★ 在嵌入式行動裝置上設計與開發跨平台Widget
★ 在 ARM 架構之嵌入式系統上實作輕量化的手持多媒體播放裝置圖形使用者介面函式庫★ 基於網路行動裝置所設計可擴展的服務品質感知GStreamer模組
★ 針對行動網路裝置開發可擴展且跨平台之GSM/HSDPA引擎★ 於單晶片多媒體裝置進行有效率之多格式解碼管理
★ IMS客戶端設計與即時通訊模組研發:個人資訊交換模組與即時訊息模組實作★ 在可攜式多媒體裝置上實作人性化的嵌入式小螢幕網頁瀏覽器
★ 以IMS為基礎之及時語音影像通話引擎的實作:使用開放原始碼程式庫★ 電子書嵌入式開發: 客制化下載服務實作, 資料儲存管理設計
★ 於數位機上盒實現有效率訊框參照處理與多媒體詮釋資料感知的播放器設計★ 具數位安全性的電子書開發:有效率的更新模組與資料庫實作
★ 適用於異質無線寬頻系統的新世代IMS客戶端軟體研發★ 在可攜式數位機上盒上設計並實作重配置的圖形使用者介面
★ Friendly GUI design and possibility support for E-book Reader based Android client★ Effective GUI Design and Memory Usage Management for Android-based Services
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 隨著科技的發展與進步,我們所能接觸到的資訊已不局限在生活周遭,以學習為例,傳統的教育方式,是由老師在教授並規劃學習的內容與進度,但網際網路的發達,藉由電腦與手機,我們能接收更多元的教材。在問題式學習中,希望能透過問題的方式來輔助學生學習以及讓他們更了解一個問題背後的知識含意。如此多元的資料來源,就需要一個好的管理,以知識為主要目標,將問題依照不同的內容來做分類,也可避免同一個知識點因為不同的表達方式而有不同的定義範圍。
本篇論文提出了一個針對中小學學習教材的分類系統。現代教材中的題目如果單單只用文字表示,有時無法清楚的表達正確的意思。所以大部分的題目會加入影像或圖片來輔助,我們的系統也針對這樣的情況,對文字與圖片分別設計了處理的模組。在分類器系統上,設計了結合卷積神經網路(Convolutional Neural Network)與支持向量機器(Support Vector Machine)架構的一個分類器模組,並使用Word2Vec作為卷積神經網路的嵌入層之權值。藉由文字與圖片資料的整合處理,以及結合兩個分類器模組的系統設計,使得分類效果能夠優於原本只使用文字做分類和只使用單一分類器之設計。
摘要(英) With the development and progress of science and technology, the information we can get is no longer limited around us. Take learning as an example, arranging the learning content and progress is done by teachers in the traditional learning method. But after the appearance of Internet, we can get a different kind of learning materials online. In Question-driven learning, students clarify and validate what they learn through answering questions. Such big amount of questions needs good management. A well-performed management could avoid the scene that learning materials with same knowledge set are defined in different section due to ambiguous expressions.
In this paper, we proposed a hybrid classification model using Convolutional Neural Network with Support Vector Machine that focuses on K-12 learning materials. We utilize Word2Vec as the initial weights for the embedding layer of CNN. In response to the current question not only contains text but also appear with image, we introduced a multi-modal preprocessing approach that first handles text and image separately, then convert them into single modality for classification. The experiment shows that the multi-modal preprocessing and the hybrid model can outperform single-modal preprocessing and individual classification model.
關鍵字(中) ★ 分類器
★ 卷積神經網路
★ 支持向量機器
★ Word2Vec
★ 問題式學習
關鍵字(英) ★ Classification
★ Convolutional Neural Network
★ Support Vector Machine
★ Word2Vec
★ Question-driven Learning
論文目次 摘要 I
Abstract II
致謝 III
Table of Contents IV
List of Figures VI
List of Tables VIII
1. Introduction 1
1.1 Background and Motivation 1
1.2 Challenge 10
1.3 Organization of Thesis 14
2. Related Work 15
2.1 Multi-modal Document 17
2.2 Hybrid CNN-SVM Model 19
3. Self-relevant CNN-SVM Model 21
3.1 System Goal 21
3.2 Development Toolkit 22
3.3 System Overview 23
3.4 Pre-processing 25
3.4.1 Text Preprocessing 27
3.4.2 Image preprocessing 29
3.4.3 Tokenization 31
3.5 Feature Extraction 32
3.5.1 Word2Vec 34
3.5.2 Convolutional Neural Network 37
3.6 Classification 40
3.6.1 Support Vector Machine 41
3.7 Hybrid CNN-SVM Model 44
3.8 Cross-topic labeling 46
3.9 Retraining Mechanism 47
4. Evaluation 48
5. Conclusion and Future Works 51
List of References 53
參考文獻 [1] Malik, S and Agarwal, A, "c" International Journal of Information and Education Technology, vol. 2, no. 5, p. 468, 2012.
[2] Jain, Ramesh, "A revolution in education," MultiMedia, IEEE, vol. 4, no. 1, pp. 1-5, 1997.
[3] Carver Jr, Curtis A and Howard, Richard A and Lane, William D, "Enhancing student learning through hypermedia courseware and incorporation of student learning styles,," Education, IEEE Transactions on, vol. 42, no. 1, pp. 33-38, 1999.
[4] Y. Kim, "Convolutional Neural Networks for Sentence Classification," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), pp. 1746-1751, 2014.
[5] Garcia, Christophe, and Manolis Delakis, "Convolutional face finder: A neural architecture for fast and robust face detection," IEEE Transactions on pattern analysis and machine intelligence 26.11, pp. 1408-1423.
[6] Ciresan, Dan Claudiu, et al, "Convolutional neural network committees for handwritten character classification," 2011 International Conference on Document Analysis and Recognition. IEEE, 2011.
[7] Matsugu, Masakazu, et al, "Subject independent facial expression recognition with robust face detection using a convolutional neural network," Neural Networks 16.5, pp. 555-559, 2003.
[8] Lawrence, Steve, et al, "Face recognition: A convolutional neural-network approach ," IEEE transactions on neural networks 8.1, pp. 98-113, 1997.
[9] Fukushima, Kunihiko, "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position," Biological cybernetics 36.4 , pp. 193-202, 1980.
[10] G. W. Smith, "Art and Artificial Intelligence," ArtEn, 27 March 2015. [Online].
[11] Long, Huizhong, and Wee Kheng Leow, "A hybrid model for invariant and perceptual texture mapping," Pattern Recognition, 2002. Proceedings. 16th International Conference on. IEEE, vol. 1, 2002.
[12] Huang, FJ and LeCun, Y, "Large-scale learning with svm and convolutional nets for generic object recognition," 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006.
[13] Nagi, Jawad, et al, "Convolutional neural support vector machines: hybrid visual pattern classifiers for multi-robot systems," Machine Learning and Applications (ICMLA), 2012 11th International Conference on, vol. 1, 2012.
[14] Niu, Xiao-Xiao and Suen, Ching Y, "A novel hybrid CNN–SVM classifier for recognizing handwritten digits," Pattern Recognition, vol. 45, no. 4, pp. 1318-1325, 2012.
[15] Sebastiani, Fabrizio, "Machine learning in automated text categorization," ACM computing surveys (CSUR) 34.1 , pp. 1-47, 2002.
[16] Soucy, Pascal and Mineau, Guy W, "Beyond TF-IDF Weighting forText Categorization in the Vector Space Model," IJCAI, vol. 5, pp. 1130-1135, 2005.
[17] McCallum, Andrew, and Kamal Nigam, "A comparison of event models for naive bayes text classification," AAAI-98 workshop on learning for text categorization, vol. 752, 1998.
[18] Kim, Sang-Bum, et al, "Some effective techniques for naive bayes text classification," IEEE transactions on knowledge and data engineering, pp. 1457-1466, 2006.
[19] Joachims, Thorsten, "Text categorization with support vector machines: Learning with many relevant features," European conference on machine learning, 1998.
[20] Johnson, Rie, and Tong Zhang, "Effective use of word order for text categorization with convolutional neural networks," arXiv preprint arXiv:1412.1058, 2014.
[21] Kim, Yoon, "Convolutional neural networks for sentence classification," arXiv preprint arXiv:1408.5882, 2014.
[22] Van Leemput, Koen, et al, "Automated model-based tissue classification of MR images of the brain," IEEE transactions on medical imaging , pp. 897-908, 1999.
[23] Rak, Rafal, Lukasz Kurgan, and Marek Reformat, "Multi-label associative classification of medical documents from medline," Fourth International Conference on Machine Learning and Applications (ICMLA′05). IEEE, 2005.
[24] De La Escalera, Arturo, et al, "Road traffic sign detection and classification," IEEE transactions on industrial electronics, pp. 848-859, 1997.
[25] Kostov, Vlaho, et al, "Travel destination prediction using frequent crossing pattern from driving history," Proceedings. 2005 IEEE Intelligent Transportation Systems, IEEE, 2005.
[26] Wu, Chun-Hsin, Jan-Ming Ho, and Der-Tsai Lee, "Travel-time prediction with support vector regression," IEEE transactions on intelligent transportation systems, pp. 276-281, 2004.
[27] Huang, Di, et al, "Local binary patterns and its application to facial image analysis: a survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), pp. 765-781, 2011.
[28] Pantic, Maja, and Leon J. M. Rothkrantz, "Automatic analysis of facial expressions: The state of the art," IEEE Transactions on pattern analysis and machine intelligence, pp. 1424-1445, 2000.
[29] Cavnar, William B and Trenkle, John M and others, "N-Gram-Based Text Categorization," Ann Arbor MI, vol. 48113, no. 2, pp. 161-175, 1994.
[30] Luo, Xi and Ohyama, Wataru and Wakabayashi, Tetsushi and Kimura, Fumitaka, "Automatic Chinese Text Classification Using Character-based and Word-based Approach," Document Analysis and Recognition (ICDAR), 2013 12th International Conference on, pp. 329-333, 2013.
[31] Lan, Man, et al. , "Supervised and traditional term weighting methods for automatic text categorization," IEEE transactions on pattern analysis and machine intelligence, pp. 721-735, 2009.
[32] Sparck Jones, Karen, "A statistical interpretation of term specificity and its application in retrieval," Journal of documentation 28.1 , pp. 11-21, 1972.
[33] Chapelle, Olivier, Patrick Haffner, and Vladimir N. Vapnik, "Support vector machines for histogram-based image classification," IEEE transactions on Neural Networks 10.5 , pp. 1055-1064, 1999.
[34] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, 2012.
[35] Ciresan, Dan Claudiu, et al, "Convolutional neural network committees for handwritten character classification," 2011 International Conference on Document Analysis and Recognition. IEEE, 2011.
[36] Barnard, Kobus, Matthew Johnson, and David Forsyth, "Word sense disambiguation with pictures," Proceedings of the HLT-NAACL 2003 workshop on Learning word meaning from non-linguistic data-Volume 6. Association for Computational Linguistics, 2003.
[37] La Cascia, Marco, Saratendu Sethi, and Stan Sclaroff, "Combining textual and visual cues for content-based image retrieval on the world wide web," Content-Based Access of Image and Video Libraries, 1998. Proceedings. IEEE , 1998.
[38] ] Denoyer, Ludovic and Vittaut, Jean-No¨el and Gallinari, Patrick and Brunessaux, Sylvie and Brunessaux, Stephan, "Structured multimedia document classification," Proceedings of the 2003 ACM symposium on Document engineering, pp. 153-160, 2003.
[39] Denoyer, Ludovic and Gallinari, Patrick, "Bayesian network model for semi-structured document classification," Information processing & management., vol. 40, no. 5, pp. 807-827, 2004.
[40] Seo, Min Joon, et al., "Diagram Understanding in Geometry Questions," AAAI. , 2014.
[41] Dalal, Mita K., and Mukesh A. Zaveri., "Automatic text classification: a technical review.," International Journal of Computer Applications 28.2 , pp. 37-40, 2011.
[42] S. Gunn, "Support Vector Machines for Classification and Regression," technical report, Information: Signals, Images, Systems (ISIS)Research Group, Univ. of Southampton, 1998.
[43] M. Law, "A Simple Introduction to Support Vector Machines".Dept. of Computer Science and Eng., Michigan State Univ., Lecture for CSE 802.
[44] Joachims, Thorsten, "Text categorization with support vector machines: Learning with many relevant features," 1998.
[45] Joachims, Thorsten, "A statistical learning learning model of text classification for support vector machines," Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2001.
[46] Tong, Simon and Koller, Daphne, "Support vector machine active learning with applications to text classification," The Journal of Machine Learning Research, vol. 2, pp. 45-66, 2002.
[47] Zharmagambetov, Arman S and Pak, Alexandr A, "Sentiment analysis of a document using deep learning approach and decision trees," 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO), pp. 1-4, 2015.
[48] Kalchbrenner, N., Grefenstette, E., & Blunsom, P., "A Convolutional Neural Network for Modelling Sentences.," Acl, pp. 655-665, 2014.
[49] Wang, Peng, et al, "Semantic clustering and convolutional neural network for short text categorization," Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 2, 2015.
[50] Fan, Kuo-Chin, Chi-Hwa Liu, and Yuan-Kai Wang, "Segmentation and classification of mixed text/graphics/image documents," Pattern Recognition Letters 15.12 , pp. 1201-1209, 1994.
[51] P. Soucy and G.W. Mimeau, "Beyond TF-IDF Weighting for TextCategorization in the Vector Space Model," Proc. 19th Intl Joint Conf. Artificial Intelligence (IJCAI 05), pp. 1130-1135, 2005.
[52] Tang, Yichuan, "Deep learning using linear support vector machines," arXiv preprint arXiv:1306.0239, 2013.
[53] Evgeniou, Theodoros and Pontil, Massimiliano and Papageorgiou, Constantine and Poggio, Tomaso, "Image representations and feature selection for multimedia database search," Knowledge and Data Engineering, IEEE Transactions on, vol. 15, no. 4, pp. 911-920, 2003.
[54] De Chazal, Philip, Maria O′Dwyer, and Richard B. Reilly, "Automatic classification of heartbeats using ECG morphology and heartbeat interval features," IEEE Transactions on Biomedical Engineering 51.7, pp. 1196-1206, 2004.
[55] Seo, Minjoon, et al, "Solving geometry problems: Combining text and diagram interpretation," Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP, 2015.
指導教授 吳曉光(Eric Hsiaokuang Wu) 審核日期 2016-7-18
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

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