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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 林智仁(Chih-Jen Lin) | |
| dc.contributor.author | Peng-Jen Chen | en |
| dc.contributor.author | 陳鵬仁 | zh_TW |
| dc.date.accessioned | 2021-05-20T20:06:32Z | - |
| dc.date.available | 2009-08-18 | |
| dc.date.available | 2021-05-20T20:06:32Z | - |
| dc.date.copyright | 2009-08-18 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-08-11 | |
| dc.identifier.citation | A. Griewank. Evaluating derivatives: principles and techniques of algorithmic differentiation. SIAM, 2000.
J. Laff erty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning (ICML), pages 282-289, 2001. C.-J. Lin and J. J. More. Newton's method for large-scale bound constrained problems. SIAM Journal on Optimization, 9:1100-1127, 1999. 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. D. C. Liu and J. Nocedal. On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45(1):503-528, 1989. A. Mccallum and W. Li. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In Seventh Conference on Natural Language Learning (CoNLL), 2003. F. Peng, F. Feng, and A. McCalum. Chinese segmentation and new word detection using conditional random fields. In Proceedings of The 20th International Conference on Computational Linguistics (COLING), pages 562-568, 2004. L. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-285, 1989. F. Sha and F. C. N. Pereira. Shallow parsing with conditional random fields. In HLT-NAACL, 2003. S. Vishwanathan, N. N. Schraudolph, M. W. Schmidt, and K. Murphy. Accelerated training of conditional random fields with stochastic gradient methods. In Proceedings of the 23rd International Conference on Machine Learning (ICML), pages 969-976, 2006. H.-J. Wang. Applying automatic differentiation and truncated newton methods to conditional random fields. Master's thesis, Department of Computer Science and Information Engineering, National Taiwan University, 2008. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9011 | - |
| dc.description.abstract | 條件隨機場是一個適合用來標記序列性資料的模組。由於考慮序列中所有可能的標籤組合，條件隨機場在學習及預測階段都非常耗時。牛頓法在最佳化的最後階段具有較快的收斂性質，因此我們採用牛頓法來解條件隨機場。海森矩陣向量乘積是整個計算過程中最耗時的部份。本篇論文提出一個新的動態規劃技巧，可以在多項式時間複雜度內完成海森矩陣向量乘積。 | zh_TW |
| dc.description.abstract | Conditional Random Fields (CRFs) is a useful technique to
label sequential data. Due to considering all label combinations of a sequence, CRFs' training and testing are time consuming. In this work, we consider a Newton method for training CRFs because of its possible fast final convergence. The computational bottleneck is on the Hessian-vector product. We propose a novel dynamic programming technique to calculate it in polynomial time. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T20:06:32Z (GMT). No. of bitstreams: 1 ntu-98-R96922049-1.pdf: 1435835 bytes, checksum: 9c807aba3e607fd8853f4108aa6b92d6 (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | TABLE OF CONTENTS
口試委員審定書 ii 摘要 iii ABSTRACT iv I.Introduction 1 II.Conditional Random Fields 3 2.1 Named-Entity Recognition Problem 3 2.2 Conditional Random Fields 5 2.3 Applying CRFs on NER Problems 6 III.Trust Region Newton Methods 8 3.1 A Trust Region Newton Method 8 3.2 Hessian-vector Product in Conjugate Gradient 10 3.3 Gradient Calculation 12 IV.Dynamic Programming Formula for Hessian-vector Product 16 4.1 Calculation of (4.3) 18 4.2 Calculation of (4.4) 23 4.3 Overall Procedure and Time/Memory Analysis 24 V.Conclusions 27 BIBLIOGRAPHY 28 | |
| dc.language.iso | en | |
| dc.title | 應用截斷牛頓法於條件隨機場 | zh_TW |
| dc.title | Newton Methods for Conditional Random Fields | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林軒田(Hsuan-Tien Lin),李育杰(Yuh-Jye Lee) | |
| dc.subject.keyword | 共軛梯度法,信賴區間牛頓法,最大熵值法,條件隨機場, | zh_TW |
| dc.subject.keyword | conjugate gradient methods,trust region Newton methods,maximum entropy,conditional random fields, | en |
| dc.relation.page | 28 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2009-08-11 | |
| 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 |
| Appears in Collections: | 資訊工程學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-98-1.pdf | 1.4 MB | Adobe PDF | View/Open |
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