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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 高成炎 | |
| dc.contributor.author | Chien-Hung Li | en |
| dc.contributor.author | 李建鴻 | zh_TW |
| dc.date.accessioned | 2021-06-08T05:18:25Z | - |
| dc.date.copyright | 2005-08-01 | |
| dc.date.issued | 2005 | |
| dc.date.submitted | 2005-07-31 | |
| dc.identifier.citation | [1] Chen, S., and Billings, S.A., [1992], Neural Networks for Nonlinear DynamicSystem Modeling and Identification, International Journal of Control, Vol. 56,No.2, pp. 319-346
[2] Broomhead, DS., and Lowe, D., [1988], Multivariable Functional Interpolationand Adaptive Networks, Complex System, Vol. 2, pp.321-355 [3] Moody, J. and Darken, C., [1989], Fast-learning in Networks of Locallyturned rocessing Units, Neual Computation, Vol 1, pp. 281-294 [4] Parlett, B.N.,[2000],The QR algorithm, Computing in Science & Engineering ,Vol 2, pp. 38-42 [5] William W. Hager ,[1988], Applied Numerical Linear Algebra, Prentice Hall Internation, pp. 192-225 [6] Hansen, L.K.; and Larsen, J.; Fog, T.,[1997], Early stop criterion from the bootstrap ensemble, Acoustics, Speech, and Signal Processing, 1997 IEEE International Conference on , Vol 4,pp. 3205 -3208 [7] Simon Haykin ,[1999], Neural Networks,A comprehensive Foundation, Prentice Hall Internation, pp. 215-216 [8] Dennis G. Zill , and Michael R. Cullen, [1992], Advanced Engineering Mathematics, PWS Publishing Company, pp.415-417 [9] Easwaran S., and Gowdy,J.N., [1992], An Improved Initialization Algorithm for Use with The K-means Algorithm for Code Book Generation, Proceeding of IEEE Southeastcon, Vol. 2, pp. 471-474 [10] T. M. Cover and P. E. Hart [1967], Nearest neighbor pattern classification, IEEE Trans. Inform. Theory, vol. IT-13, pp. 21-27. 57 [11] Simon Haykin ,[1999], Neural Networks,A comprehensive Foundation,Prentice Hall Internation, pp. 213-214 [12] Jose C. Principe ,and Neil R. Euliano,and W.Curt Lefebvre,[2000], Neural and Adaptive System , John Wiley & Sons ,inc., pp.8-9 [13] Mark L. Berenson ,and David M. Levine, [1999], Basic Business Statistics Concepts and Applications , 7nd Edition, Prentice Hall, pp. 811-843 [14] Douglas C. Montgomery ,George C.Runger, [1994], Applied Statistics And Probaility For Engineers,John Wiley & Sons,Inc pp.192-225 [15] Vladimir Cherkassky, [1996] , Comparison of Adaptive Methods for Function Estimation form Samples, IEEE Transactions on Neural Networks, Vol. 7, pp. 969-984 [16] J. Barry Gomm, and Ding Li Yu, [2000], Selecting Radial Basis Function Network Centers with Recursive Orthogonal Least Squares Training, IEEE Transactions on Neural Networks , Vol. 11, pp. 306-314 [17] Peter J.Rousseeuw, and Annick M. Leroy, [1987], Robust Regression and Outlier Detection, John Wiley & Sons,pp.75-84 [18] Bradley Efron, and RoberT J. Tibshirani, [1993], An Introduction to the Bootstrap, Chapman & Hall,Inc, pp.105-123 [19] M.T, and Demuth, H.B., American Control Confereence, [1999], Neural Network for Control, Proceedings of the 1999 , Volume: 3 , 1999 Page(s): 1642 -1656 vol.3 [20] Han-Yu Chuang, Hongfang Liu, Cameron McMunn-Coffran, Cheng-Yan Kao, and D. Frank Hsu, Identifying Significant Genes from Microarray Data, Fourth IEEE Symposium on Bioinformatics and Bioengineering (BIBE'04) 05 19 - 05, 2004, p. 358 [21]Topon Kumar Paul and Hitoshi lba, Extraction of Informative Genes from Microarray Data， [22]Jinn-Moon Yang, A Family Competition Evolutionary Approach of Global Optimization in Neural Networks, Optical Thin-film Design, and Structure-based Drug Design [23]Elena Marchiori and Michele sebag, Bayesian Learning with Local Support Vector Machines for Cancer Classification with Gene Expression Data. [24] T.R. Golub, et al. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 286:531-537, October 1999 [25] Laura J. van't Veer, et al. Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer, Nature, 415:530-536, 2002 [26] Gavin J.Gordon, et al. Translation of Microarray Data into Clinically Relevant Cancer Diagnostic Tests Using Gege Expression Ratios in Lung Cancer And Mesothelioma. Cancer Research, 62:4963-4967, 2002 [27] Gene Expression Correlates of Clinical Prostate Cancer Behavior. Cancer Cell, 1:203-209, March, 2002 [28] Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403:503-511, Feburary 2000 [29] C. L. Nutt, et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Research, 63(7):1602–1607, 2003. [30]Mao-Cheng Wu, Evolutionary Radial Basis Function Networks for Nonlinear Time Series Prediction, Master Thesis of Department of Computer Science and Information Engineering, National Taiwan University 1997 [31] Chen, S., and Billings, S.A., 1992, “Neural Networks for Nonlinear Dynamic System Modeling and Identification,” International Journal of Control, Vol. 56, No.2, pp. 319-346 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/24204 | - |
| dc.description.abstract | 本文對於使用微矩陣資料進行癌症分類提供一種新的方法，演化徑向基函網路（Evolutionary Radial Basis Function Network - ERBFN）。演化徑向基底函數網路是普通的徑向基底函數網路的一個顯著的改進。ERBFN從傳統的分群演算法開始, 接著對Radial Basis Function Network的隱藏層進行最佳化，並且使用監督式學習策略調整網路連接的加權值。目前這個方法也已經成功的應用於真實的癌症資料的分類上。根據我們的評估，ERBFN的正確性可以跟已SVM為基礎的分類相比較。 | zh_TW |
| dc.description.abstract | In this work, we proposed a novel method, Evolutionary Radial Basis Function Network (ERBFN), for classification of cancer types with microarray gene expression data. Evolutionary Radial Basis Function Network is a significant improvement over ordinary Radial Basis Function Network. Starting with traditional clustering algorithm, ERBFN optimized the hidden layer of Radial Function Network, and used supervised learning strategy to fine-tune the network connection weights. This method has been successfully applied to classification of real-world cancer data. Our assessment has revealed that the accuracy of ERBFN is comparable to that of support vector machine based classification. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T05:18:25Z (GMT). No. of bitstreams: 1 ntu-94-R92922095-1.pdf: 692114 bytes, checksum: 22f96232dc14e965b7da93e727f743b2 (MD5) Previous issue date: 2005 | en |
| dc.description.tableofcontents | 摘要 I
ABSTRACT II 致謝 III 目錄 V 圖目錄 VII 表目錄 VIII 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 1 1.3 本論文內容與架構 2 第二章 相關研究 3 2.1 基因微陣列 3 2.1.1 概要 4 2.1.1 微陣列實驗的製作過程 5 2.1.2 基因微陣列資料型態 6 2.2 徑向基底函數網路 7 2.2.1 神經元的模型 8 2.2.2 徑向基底函數網路 11 2.3 癌症分類 12 2.3.1 統計區別分析分類法 14 2.3.2 機器學習分類法 14 2.3.3 類神經網路分類法 16 2.4 交互認證(CROSS VALIDATION) 16 第三章 研究方法 18 3.1 微矩陣資料 19 3.2 基因選取 19 3.3 K-MEANS CLUSTERING 20 3.4 演化過程 21 3.4.2 隱藏層節點中心數目調整 23 3.4.3 調整隱藏層節點中心寬度 24 3.4.4 基因交換（交配Crossover） 25 3.4 連線權重學習 26 3.6 合適值計算 27 第四章 實證研究 29 4.1 資料來源 29 4.2 分析結果 31 第五章 結論與未來工作 34 5.1 結論 34 5.2 未來工作 34 參考文獻 35 | |
| dc.language.iso | zh-TW | |
| dc.subject | 徑向基底函數網路 | zh_TW |
| dc.subject | 微陣列 | zh_TW |
| dc.subject | 癌症 | zh_TW |
| dc.subject | Microarray | en |
| dc.subject | Cancer | en |
| dc.subject | RBF | en |
| dc.title | 以演化徑向基函網路進行癌症分類之研究 | zh_TW |
| dc.title | Cancer Classification with Evolutional Radial Basis Function Network | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭明良,陳朝欽,黃奇英,廖宜恩 | |
| dc.subject.keyword | 微陣列,癌症,徑向基底函數網路, | zh_TW |
| dc.subject.keyword | Microarray,Cancer,RBF, | en |
| dc.relation.page | 38 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2005-07-31 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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