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研究生:邱韵瓴
研究生(外文):Chiou, Yun-Ling
論文名稱:可提升分類正確率的基於特徵空間圖資訊的集群間距調整方法
論文名稱(外文):Adjusting between-cluster Margins based on Graph Information of Feature Domain for Boosting Classification Accuracy
指導教授:邵皓強
指導教授(外文):Shao, Hao-Chiang
口試委員:陳祝嵩林嘉文
口試委員(外文):Chen, Chu-SongLin, Chia-Wen
口試日期:2020-07-29
學位類別:碩士
校院名稱:輔仁大學
系所名稱:統計資訊學系應用統計碩士班
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:80
中文關鍵詞:分類不平衡資料分群特徵空間
外文關鍵詞:classificationunbalance dataclusteringgraphfeature domain
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第壹章 緒論 1
第一節 研究背景 1
第二節 研究目的 4
第三節 論文架構 5
第貳章 文獻探討 6
第一節 LMLE 6
第二節 Wide Residual Networks 8
第三節 Triplet Loss 11
第參章 研究方法 13
第一節 研究架構 13
第二節 分群 14
第三節 五重孿生網路 17
第肆章 實驗 21
第一節 研究資料 21
第二節 AOI瑕疵分類 22
第三節 晶圓圖分類 52
第伍章 結論與未來展望 78
第一節 結論 78
第二節 未來展望 78
參考文獻 79
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
Chen, C., Liaw, A., & Breiman, L. (2004). Using random forest to learn imbalanced data. University of California, Berkeley, 110(1-12), 24.
Chopra, S., Hadsell, R., & LeCun, Y. (2005, June). Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (Vol. 1, pp. 539-546). IEEE.
Cui, Y., Jia, M., Lin, T. Y., Song, Y., & Belongie, S. (2019). Class-balanced loss based on effective number of samples. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 9268-9277).
Dueck, D. (2009). Affinity propagation: clustering data by passing messages (p. 144). Toronto: University of Toronto.
Han, H., Wang, W. Y., & Mao, B. H. (2005, August). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In International conference on intelligent computing (pp. 878-887). Springer, Berlin, Heidelberg.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Huang, C., Li, Y., Change Loy, C., & Tang, X. (2016). Learning deep representation for imbalanced classification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5375-5384).
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. science, 290(5500), 2323-2326.
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823).
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
Tenenbaum, J. B., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. science, 290(5500), 2319-2323.
Wang, K., Zhang, J., Li, D., Zhang, X., & Guo, T. (2008). Adaptive affinity propagation clustering. arXiv preprint arXiv:0805.1096.
Zadrozny, B., Langford, J., & Abe, N. (2003, November). Cost-sensitive learning by cost-proportionate example weighting. In Third IEEE international conference on data mining (pp. 435-442). IEEE.
Zagoruyko, S., & Komodakis, N. (2016). Wide residual networks. arXiv preprint arXiv:1605.07146.

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