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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林智仁 | |
| dc.contributor.author | Tian-Liang Huang | en |
| dc.contributor.author | 黃天亮 | zh_TW |
| dc.date.accessioned | 2021-05-20T21:28:13Z | - |
| dc.date.available | 2010-08-20 | |
| dc.date.available | 2021-05-20T21:28:13Z | - |
| dc.date.copyright | 2010-08-20 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-19 | |
| dc.identifier.citation | A. L. Berger, V. J. Della Pietra, and S. A. Della Pietra. A maximum entropy
approach to natural language processing. Computational Linguistics, 22(1):39– 71, 1996. B. E. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal mar- gin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144–152. ACM Press, 1992. L. Bottou, C. Cortes, J. Denker, H. Drucker, I. Guyon, L. Jackel, Y. LeCun, U. Muller, E. Sackinger, P. Simard, and V. Vapnik. Comparison of classifier meth- ods: a case study in handwriting digit recognition. In International Conference on Pattern Recognition, pages 77–87. IEEE Computer Society Press, 1994. K.-W. Chang, C.-J. Hsieh, and C.-J. Lin. Coordinate descent method for large- scale L2-loss linear SVM. Journal of Machine Learning Research, 9:1369–1398, 2008. URL http://www.csie.ntu.edu.tw/~cjlin/papers/cdl2.pdf. C. Cortes and V. Vapnik. Support-vector network. Machine Learning, 20:273– 297, 1995. K. Crammer and Y. Singer. On the learnability and design of output codes for multiclass problems. In Computational Learing Theory, pages 35–46, 2000. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIB- LINEAR: A library for large linear classification. Journal of Machine Learn- ing Research, 9:1871–1874, 2008. URL http://www.csie.ntu.edu.tw/~cjlin/ papers/liblinear.pdf. C.-J. Hsieh, K.-W. Chang, C.-J. Lin, S. S. Keerthi, and S. Sundararajan. A dual coordinate descent method for large-scale linear SVM. In Proceedings of the Twenty Fifth International Conference on Machine Learning (ICML), 2008. URL http://www.csie.ntu.edu.tw/~cjlin/papers/cddual.pdf. C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2):415–425, 2002. F.-L. Huang, C.-J. Hsieh, K.-W. Chang, and C.-J. Lin. Iterative scaling and coordinate descent methods for maximum entropy. In Proceedings of the 47th Annual Meeting of the Association of Computational Linguistics (ACL), 2009. Short paper. T. Joachims. Training linear SVMs in linear time. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006. D. Jurafsky and J. H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recogni- tion. Prentice Hall, second edition, 2008. S. S. Keerthi, S. Sundararajan, K.-W. Chang, C.-J. Hsieh, and C.-J. Lin. A sequential dual method for large scale multi-class linear SVMs. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008. URL http://www.csie.ntu.edu.tw/~cjlin/papers/sdm_ kdd.pdf. S. Knerr, L. Personnaz, and G. Dreyfus. Single-layer learning revisited: a stepwise procedure for building and training a neural network. In J. Fogelman, editor, Neu- rocomputing: Algorithms, Architectures and Applications. Springer-Verlag, 1990. C.-J. Lin, R. C. Weng, and S. S. Keerthi. Trust region Newton method for large- scale logistic regression. Journal of Machine Learning Research, 9:627–650, 2008. URL http://www.csie.ntu.edu.tw/~cjlin/papers/logistic.pdf. E. Mayoraz and E. Alpaydin. Support vector machines for multi-class classifica- tion. In IWANN (2), pages 833–842, 1999. URL http://citeseer.nj.nec.com/ mayoraz98support.html. R. Memisevic. Dual optimization of conditional probability models. Technical report, Department of Computer Science, University of Toronto, 2006. R. Rifkin and A. Klautau. In defense of one-vs-all classification. Journal of Machine Learning Research, 5:101–141, 2004. ISSN 1533-7928. S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: primal estimated sub- gradient solver for SVM. In Proceedings of the Twenty Fourth International Con- ference on Machine Learning (ICML), 2007. J. Weston and C. Watkins. Multi-class support vector machines. Technical Report CSD-TR-98-04, Royal Holloway, 1998. H.-F. Yu, F.-L. Huang, and C.-J. Lin. Dual coordinate descent methods for logistic regression and maximum entropy models. Technical report, Depart- ment of Computer Science, National Taiwan University, March 2010. URL http://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/10421 | - |
| dc.description.abstract | The classification problem appears in many applications such as document classification and web page search. Support vector machine(SVM) is one of the most popular tools used in classification task. One of the component in SVM is the kernel trick. We use kernels to map data into a higher dimentional space. And this technique is applied in non-linear SVMs. For large-scale sparce data, we use the linear kernel to deal with it. We call such SVM as the linear SVM. There are many kinds of SVMs in which different loss functions are applied. We call these SVMs as L1-SVM and L2-SVM in which L1-loss and L2-loss functions are used respectively. We can also apply SVMs to deal with multi-class classification with one-against-one or one-against-all approaches. In this thesis several models such as logistic regression, L1-SVM, L2-SVM, Crammer and Singer, and maximum entropy will be compared in the multi-class classification task. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T21:28:13Z (GMT). No. of bitstreams: 1 ntu-99-R97922002-1.pdf: 1686282 bytes, checksum: 5661b069fab83159f4cf312aa1d6b8e5 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 口試委員會審定書...........................................i
中文摘要..................................................ii Abstract.................................................iii List of tables............................................iv CHAPTER I. Introduction..........................................1 II. Models................................................4 2.1 Support Vector Machine................................4 2.2 Crammer and Singer....................................6 2.3 Maximum entropy (ME)..................................7 III. Methods...............................................9 3.1 Trust Region Newton Method (TRON).....................9 3.1.1 Logistic Regression (LR)..........................10 3.1.2 L2-loss Support Vector Machine (L2-SVM)...........11 3.2 Coordinate Descent...................................11 3.2.1 Support Vector Machine Dual with L1-loss and L2-loss ..12 3.2.2 Crammer and Singer................................13 3.2.3 Maximum entropy (ME)..............................14 I.V. Feature of Different Schemes.........................17 V. Experiment...........................................20 5.1 Data Sets...........................................20 5.2 Setting.............................................21 5.3 Comparison..........................................22 V.I. Discussion and Conclusion............................27 BIBLIOGRAPHY..............................................29 | |
| dc.language.iso | en | |
| dc.title | 二階正規化多標籤線性分類器比較 | zh_TW |
| dc.title | Comparison of L2-Regularized Multi-Class Linear Classifiers | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林軒田,鮑興國 | |
| dc.subject.keyword | 線性分類模型,線性支持向量機,多標籤分類,最大熵方法,座標下降法, | zh_TW |
| dc.subject.keyword | linear classification,linear support vector machines,multi-class classification,maximum entropy,coordinate descent, | en |
| dc.relation.page | 30 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2010-08-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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|---|---|---|---|
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