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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林智仁(Chih-Jen Lin) | |
| dc.contributor.author | Tzu-Jung Liu | en |
| dc.contributor.author | 劉子榮 | zh_TW |
| dc.date.accessioned | 2021-06-13T17:29:17Z | - |
| dc.date.available | 2004-07-28 | |
| dc.date.copyright | 2004-07-28 | |
| dc.date.issued | 2004 | |
| dc.date.submitted | 2004-07-20 | |
| dc.identifier.citation | [1] R. R. Bailey, E. J. Pettit, R. T. Boro cho , M. T. Manry, and X. Jiang. Automatic recognition of usgs land use/cover categories using statistical and neural networks classi ers. In SPIE OE/Aerospace and Remote Sensing , Bellingham, WA, 1993. SPIE.
[2] C. L. Blake and C. J. Merz. UCI rep ository of machine learning databases. Technical rep ort, University of California, Department of Information and Computer Science, Irvine, CA, 1998. Available at http://www.ics.uci.edu/~mlearn/MLRepository.html. [3] G. W. Brier. Veri cation of forecasts expressed in probabilities. Monthly Weather Review, 78:1–3, 1950. [4] C.-C. Chang. Study on new supp ort vector machines. Master’s thesis, Department of Computer Science and Information Engineering, National Taiwan University, 2001. [5] C.-C. Chang and C.-J. Lin. IJCNN 2001 challenge: Generalization ability and text deco ding. In Proceedings of IJCNN. IEEE, 2001. [6] C.-W. Hsu, C.-C. Chang, and C.-J. Lin. A practical guide to supp ort vector classi cation. Technical rep ort, 2003. [7] C.-J. Lin. Formulations of supp ort vector machines: a note from an optimization p oint of view. Neural Computation, 13(2):307–317, 2001. [8] H.-T. Lin, C.-J. Lin, and R. C. Weng. A note on Platt’s probabilistic outputs for supp ort vector machines. Technical rep ort, Department of Computer Science and Information Engineering, National Taiwan University, 2003. [9] S. G. Nash and A. Sofer. Linear and Nonlinear Programming. McGraw-Hill, 1996. [10] J. No cedal and S. J. Wright. Numerical Optimization. Springer-Verlag, New York, NY, 1999. [11] J. Platt. Probabilistic outputs for supp ort vector machines and comparison to regularized likeliho o d metho ds. In A. Smola, P. Bartlett, B. Sch‥olkopf, and D. Schuurmans, editors, Advances in Large Margin Classi ers, Cambridge, MA, 2000. MIT Press. [12] W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes: The Art of Scienti c Computing. Cambridge University Press, Cambridge (UK) and New York, 2nd edition, 1992. [13] D. Prokhorov. IJCNN 2001 neural network comp etition. Slide presentation in IJCNN’01, Ford Research Lab oratory, 2001. http://www.geocities.com/ijcnn/nnc_ijcnn01.pdf. [14] G. R‥atsch. Ensemble learning metho ds for classi cation. april 1998. Diploma thesis (in german). [15] G. R‥atsch, T. Ono da, and K.-R. M ‥uller. Soft margins for AdaBo ost. Technical Rep ort NC-TR-1998-021, Department of Computer Science, Royal Holloway, University of London, Egham, UK, Aug. 1998. Submitted to Machine Learning. [16] W. S. Sarle. Neural Network FAQ, 1997. Perio dic p osting to the Usenet news group comp.ai.neural-nets. [17] V. Vapnik. Statistical Learning Theory. Wiley, New York, NY, 1998. [18] T.-F. Wu, C.-J. Lin, and R. C. Weng. Probability estimates for multi-class classifcation by pairwise coupling. In Proceedings of NIPS 2003, 2003. [19] B. Zadrozny and C. Elkan. Transforming classi er scores into accurate multi-class probability estimates. In Proceedings of the Eighth International Conference on Know ledge Discovery and Data Mining, 2002. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/39469 | - |
| dc.description.abstract | Support vector machine (SVM) is a promising
technique for data classification and regression. However, it provides only decision values but not posterior probability estimates. As many applications require probability outputs, it is essential to study how to transform SVM outputs to probability values. In this thesis, we study and compare various methods. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T17:29:17Z (GMT). No. of bitstreams: 1 ntu-93-R91546024-1.pdf: 243473 bytes, checksum: 2535330b0cf35bda2dc81fceed376c51 (MD5) Previous issue date: 2004 | en |
| dc.description.tableofcontents | Chapter 1. Introduction - 1
Chapter 2. Support Vector Machine - 3 Chapter 3. Probability Methods - 8 Chapter 4. Methods of Experiments - 21 Chapter 5. Results - 23 Chapter 6. Conclusions - 41 | |
| dc.language.iso | zh-TW | |
| dc.subject | 機率輸出 | zh_TW |
| dc.subject | SVM | en |
| dc.title | Probabilistic Output of Support Vector Machines | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 91-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 李育杰(Yuh-Jye Lee),雀v南(Chun-Nan Hsu), | |
| dc.contributor.oralexamcommittee | 李育杰(yuh-jye@mail.ntust.edu.tw),雀v南(chunnan@iis.sinica.edu.tw), | |
| dc.subject.keyword | 機率輸出, | zh_TW |
| dc.subject.keyword | SVM, | en |
| dc.relation.page | 43 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2004-07-20 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| 顯示於系所單位： | 工業工程學研究所 | |
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