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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林智仁 | |
| dc.contributor.author | Po-Han Chung | en |
| dc.contributor.author | 鐘博瀚 | zh_TW |
| dc.date.accessioned | 2021-06-13T00:05:05Z | - |
| dc.date.available | 2011-08-18 | |
| dc.date.copyright | 2011-08-18 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-08 | |
| dc.identifier.citation | N. Askitis. Fast and compact hash tables for integer keys. In Proceeding of the
32nd Australasian Computer Science Conference, pages 101-110, 2009. B. E. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144-152. ACM Press, 1992. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. Y.-W. Chang. Low-degree polynomial mapping of data for svm. Master's thesis, Department of Computer Science and Information Engineering, National Taiwan University, 2009. Y.-W. Chang, C.-J. Hsieh, K.-W. Chang, M. Ringgaard, and C.-J. Lin. Training and testing low-degree polynomial data mappings via linear SVM. Journal of Machine Learning Research, 11:1471-1490, 2010. URL http://www.csie.ntu. edu.tw/~cjlin/papers/lowpoly_journal.pdf. C. Cortes and V. Vapnik. Support-vector network. Machine Learning, 20:273-297, 1995. K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer. Online passive-aggressive algorithms. Journal of Machine Learning Research, 7:551-585, 2006. R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research, 6:1889-1918, 2005. URL http://www.csie.ntu.edu.tw/~cjlin/papers/quadworkset. pdf. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classi cation. Journal of Machine Learn-ing Research, 9:1871-1874, 2008. URL http://www.csie.ntu.edu.tw/~cjlin/ papers/liblinear.pdf. Y. Goldberg and M. Elhadad. splitSVM: Fast, space-e cient, non-heuristic, polynomial kernel computation for NLP applications. In Proceedings of the 46st An-nual Meeting of the Association of Computational Linguistics (ACL), 2008. 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. H. Isozaki and H. Kazawa. E cient support vector classi ers for named entity recognition. In Proceedings of COLING, pages 390-396, 2002. T. Joachims. Making large-scale SVM learning practical. In B. Sch olkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods-Support Vector Learning, pages 169-184, Cambridge, MA, 1998. MIT Press. S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. Improvements to Platt's SMO algorithm for SVM classi er design. Neural Computation, 13:637-649, 2001. T. Kudo and Y. Matsumoto. Japanese dependency structure analysis based on support vector machines. In Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora, 2000. T. Kudo and Y. Matsumoto. Fast methods for kernel-based text analysis. In Proceedings of the 41st Annual Meeting of the Association of Computational Lin- guistics (ACL), 2003. P. Li, A. Shrivastava, J. Moore, and A. C. K onig. Hashing algorithms for largescale learning. Technical report, Cornell University, 2011. URL http://www. stat.cornell.edu/~li/reports/HashLearning.pdf. E. F. T. K. Sang. Introduction to the conll-2002 shared task: Languageindependent named entity recognition. In Proceeding of CoNLL-2002, 2000. E. F. T. K. Sang and S. Buchholz. Introduction to the conll-2000 shared task: Chunking. In Proceeding of CoNLL-2000 and LLL-2000, pages 127-132, 2000. M. Sassano. Linear-time dependency analysis for japanese. In Proceeding of COL- ING, pages 8-14, 2004. A. Sumida, N. Yoshinaga, and K. Torisawa. Boosting precision and recall of hyponymy relation acquisition from hierarchical layouts in wikipedia. In Proceeding of LREC, pages 2462-2469, 2008. N. Yoshinaga and M. Kitsuregawa. Kernel slicing: Scalable online training with conjunctive features. In Proceedings of the 23rd International Conference on Com- putational Linguistics, pages 1245-1253, 2010. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28319 | - |
| dc.description.abstract | Recently, many people handle natural language processing (NLP) tasks via support vector machines (SVM) with polynomial kernels. However, kernel computation is time consuming. Chang et al. (2010) have proposed mapping data by low-degree polynomial functions and applying fast linear-SVM methods. For data with many features, they have considered condensing data to effectively solve some memory and computational difficulties. In this thesis, we investigate Chang et al.'s methods and give implementation details. We conduct experiments on four NLP tasks to show the viability of our implementation. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T00:05:05Z (GMT). No. of bitstreams: 1 ntu-100-R98944031-1.pdf: 2284755 bytes, checksum: 29eaac300852983bd2bd8123f2e59567 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 口試委員會審定書: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : i
中文摘要: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :ii ABSTRACT : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : iii LIST OF TABLES : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : vi CHAPTER I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 II. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 Linear SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Nonlinear SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Decomposition Methods for Nonlinear SVM . . . . . . . . . . . 6 2.4 Decomposition Methods for Linear SVM . . . . . . . . . . . . . 8 III. Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1 Low-degree Polynomial Mappings . . . . . . . . . . . . . . . . . 10 3.2 Importance of Condensing Features . . . . . . . . . . . . . . . . 11 3.3 Training by Decomposition Methods . . . . . . . . . . . . . . . 12 3.4 Implementation of the Hash Table . . . . . . . . . . . . . . . . 14 3.5 Implementation for Binary Features . . . . . . . . . . . . . . . 15 IV. Kernel Slicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.1 Kernel Expansion . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.2 Kernel Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.3 Kernel Slicing . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 V. Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5.1 Task Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . 21 5.2 Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 VI. Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . 29 BIBLIOGRAPHY : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 30 | |
| dc.language.iso | zh-TW | |
| dc.subject | 多項式映射 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | Natural language processing | en |
| dc.subject | Polynomial mapping | en |
| dc.subject | Support vector machine | en |
| dc.title | 低階多項式自然語言處理之資料映射同時利用雜湊達成特
徵空間壓縮 | zh_TW |
| dc.title | Low-degree Polynomial Mapping of NLP Data and
Features Condensing by Hashing | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林軒田,李育杰 | |
| dc.subject.keyword | 自然語言處理,支持向量機,多項式映射, | zh_TW |
| dc.subject.keyword | Natural language processing,Support vector machine,Polynomial mapping, | en |
| dc.relation.page | 31 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-08-08 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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| ntu-100-1.pdf 未授權公開取用 | 2.23 MB | Adobe PDF |
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