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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 林智仁(Chih-Jen Lin) | |
dc.contributor.author | Xiaocong Zhou | en |
dc.contributor.author | 周驍聰 | zh_TW |
dc.date.accessioned | 2021-06-16T09:15:54Z | - |
dc.date.available | 2020-07-20 | |
dc.date.copyright | 2017-07-20 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-07-17 | |
dc.identifier.citation | A. Appleby. Murmurhash 2.0, 2008.
B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, pages 144–152. ACM, 1992. G. Cantor. Ein beitrag zur mannigfaltigkeitslehre. Journal für die reine und angewandte Mathematik, 84:242–258, 1877. URL http://eudml.org/doc/148353. 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, 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(Apr):1471–1490, 2010. P.-H. Chung and C.-J. Lin. Low-degree polynomial mapping of nlp data and features condensing by hashing. NTU master thesis, 2011. C. Cortes and V. Vapnik. Support-vector networks. Machine learning, 20(3):273– 297, 1995. Q. Dang, T. Polk, and D. R. Brown. Internet x. 509 public key infrastructure: Additional algorithms and identifiers for dsa and ecdsa. 2010. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. Liblinear: A library for large linear classification. Journal of machine learning research, 9 (Aug):1871–1874, 2008. R. Fueter and G. Pólya. Rationale abzählung der gitterpunkte. Vierteljschr. Natur- forsch. Ges. Zürich, 58:380–386, 1923. 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 25th international conference on Machine learning, pages 408–415. ACM, 2008. H.-Y. Huang and C.-J. Lin. Linear and kernel classification: When to use which? In Proceedings of the 2016 SIAM International Conference on Data Mining, pages 216–224. SIAM, 2016. J. Langford, L. Li, and T. Zhang. Sparse online learning via truncated gradient. Journal of Machine Learning Research, 10(Mar):777–801, 2009. L. C. Noll. Fnv hash. FNV Hash, 2005. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59090 | - |
dc.description.abstract | Nonlinear mappings have long been used in data classification to handle linearly inseparable problems. Low-degree polynomial mappings are a widely used one among them, which enjoys less time and space consumption and may sometimes achieve accuracy close to that of using highly nonlinear kernels. However, the explicit form of polynomially mapped data for large data sets can also meet memory or computational difficulties. To solve this, hash functions like murmur and fnv hash are used in some packages like vowpal wabbit to have flexible memory usage. In this thesis, we propose a new hash function which is faster and could achieve the same performance. The results are validated in experiments on many datasets. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T09:15:54Z (GMT). No. of bitstreams: 1 ntu-106-R04922144-1.pdf: 247717 bytes, checksum: cbcc88a8383ab91127e075da248f3663 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | Abstract i
1 Introduction 1 2 TheModel 3 2.1 LinearandNonlinearSVM........................ 3 2.2 TrainingSVM............................... 4 2.3 Using Linear SVM for Low-degree Polynomial Data Mappings . . . . 5 2.4 ImplementationIssues .......................... 6 3 Feature Condensing and Hash Functions 7 3.1 FeatureCondensing............................ 7 3.1.1 DictionaryMethod........................ 7 3.2 HashFunctions .............................. 8 3.2.1 MurmurHash and FNVhash .................. 9 3.2.2 CantorPairingFunction..................... 12 4 Analysis 14 4.1 CollisionRate............................... 14 4.2 Consistency of the Estimator with Cantor Pairing Function . . . . . . 15 5 Experiments 18 5.1 ExperimentalSettings .......................... 18 5.2 CollisionRate............................... 19 5.3 TrainingTimeandAccuracy....................... 21 6 Conclusions 26 Bibliography 27 | |
dc.language.iso | en | |
dc.title | 大規模線性分類資料低階多項式映射中雜湊函數之應用 | zh_TW |
dc.title | Hash Functions for Polynomial Feature Mapping in Large Scale Linear Classification | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林軒田(Hsuan-Tien Lin),李育杰(Yuh-Jye Lee) | |
dc.subject.keyword | 低階多項式映射,雜湊函數, | zh_TW |
dc.subject.keyword | low-degree polynomial mapping,hash functions, | en |
dc.relation.page | 28 | |
dc.identifier.doi | 10.6342/NTU201701597 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-07-18 | |
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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