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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許永真(Yung-jen Hsu) | |
| dc.contributor.author | Shih-Chieh Yen | en |
| dc.contributor.author | 顏士傑 | zh_TW |
| dc.date.accessioned | 2021-06-12T18:19:49Z | - |
| dc.date.available | 2007-09-03 | |
| dc.date.copyright | 2007-09-03 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-08-24 | |
| dc.identifier.citation | [1] O. Amft, M. StAager, P. Lukowicz, and G. TrAoster. Analysis of chewing sounds for dietary monitoring. In UbiComp 2005: Proceedings of the 7th International Conference on Ubiquitous Computing, pages 56-72. Springer: Lecture Notes in Computer Science, September 2005.
[2] L. Bao and S. S. Intille. Activity recognition from user-annotated acceleration data. In Proceedings of the 2nd International Conference on Pervasive Computing (Pervasive 2004), pages 1-17, 2004. [3] J. Chen, A. H. Kam, J. Zhang, N. Liu, and L. Shue. Bathroom activity monitoring based on sound. In Proceedings of the 3nd International Conference on Pervasive Computing (Pervasive 2005), pages 47-61, 2005. [4] Y.-H. Chen. Emotion recognition from physiological sensor data-learning and application. Master's thesis, National Taiwan University, 2007. [5] Y.-W. Chen and C.-J. Lin. Combining SVMs with various feature selection strategies. In I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, editors, Feature extraction, foundations and applications. Springer, 2006. [6] R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals Eugen, 7:179-188, 1936. [7] I. Guyon, J.Weston, S. Barnhill, and V. Vapnik. Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learn, 46:389-422, January 2002. [8] A. S. Hedayat, N. J. A. Sloane, and J. Stufken. Orthogonal Arrays: Theory and Applications. Springer-Verlag, New York, 1999. [9] M. Heiler, D. Cremers, and C. SchnAorr. Efficient feature subset selection for support vector machines. Technical report, University of Mannheim, Computer Science, Technical Report, Nr. Nr. 21, 2001. [10] E. A. Heinz, K. S. Kunze, M. Gruber, D. Bannach, and P. Lukowicz. Using wearable sensors for real-time recognition tasks in games of martial arts - an initial experiment. In Proceedings of the 2nd IEEE symposium on Computational Intelligence and Games (CIG 2006), pages 98-102, 2006. [11] N. Kern, B. Schiele, H. Junker, P. Lukowicz, and G. Troster. Wearable sensing to annotate meeting recordings. Personal Ubiquitous Comput, 7:263-274, 2003. [12] R. Kohavi and G. H. John. Wrappers for feature subset selection. Arti‾cial Intelligent, 97(1-2):273-324, 1997. [13] J. Lester, T. Choudhury, and G. Borriello. A practical approach to recognizing physical activities. In Proceedings of the 4th International Conference on Pervasive Computing (Pervasive 2006), pages 1-16, 2006. [14] Y. Li and D. C. Instructional video content analysis using audio information. In IEEE Transactions on Audio, Speech and Language Processing, volume 14, pages 2264-2274, November 2006. [15] P. Lukowicz, J.Ward, H. Junker, M. StAager, G. TrAoster, A. Atrash, and T. Starner. Recognizing workshop activity using body worn microphones and accelerometers. In Proceedings of the 2nd International Conference on Pervasive Computing (Pervasive 2004), pages 18-32, 2004. [16] J. MAantyjAarvi, J. Himberg, and T. SeppAanen. Recognizing human motion with multiple acceleration sensors. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pages 747-752, 2001. [17] D. Patterson, D. Fox, H. Kautz, and M. Philipose. Fine-grained activity recognition by aggregating abstract object usage. In Proceedings of the 9th IEEE International Symposium on Wearable Computers (ISWC 2005), pages 44-51, 2005. [18] J. K. Perng, B. Fisher, S. Hollar, and K. S. J. Pister. Acceleration sensing glove (asg). In Proceedings of the 3th IEEE International Symposium on Wearable Computers (ISWC 1999), pages 178-180, 1999. [19] M. Philipose, K. Fishkin, M. Perkowitz, D. Patterson, D. Fox, H. Kautz, and D. Hahnel. Inferring activities from interactions with objects. In Proceedings of the 2nd International Conference on Pervasive Computing (Pervasive 2004), volume 3, pages 50-57, 2004. [20] P. Pudil, J. Novovi·cov?a, and J. Kittler. Floating search methods in feature selection. Pattern Recognition Letters, 15(11):1119-1125, 1994. [21] C. Randell and H. Muller. Context awareness by analysing accelerometer data. In Proceedings of the 4th IEEE International Symposium on Wearable Computers (ISWC 2000), pages 175-176, 2000. [22] A. Schmidt, H.-W. Gellersen, and C. Merz. Enabling implicit human computer interaction - a wearable RFID-Tag reader. In ISWC2000, 2000. [23] G. Taguchi. Introduction to Quality Engineering. UNIPUB/Kraus International, White Plains, New York, 1986. [24] G. Taguchi. System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Cost. UNIPUB/Kraus International, White Plains, New York, 1987. [25] K. Van Laerhoven and O. Cakmakci. What shall we teach our pants? In Proceedings of the 4th IEEE International Symposium on Wearable Computers (ISWC 2000), pages 77-83, 2000. [26] J. H. van Lint and R. M. Wilson. A Course in Combinatorics. Cambridge University Press, 1992. [27] P. Viola and J. M. Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001. [28] E. Vyzas. Recognition of emotion and cognitive states using physiological data. Master's thesis, Mechanical Engineering, in Massachusetts Institute of Technology, 1999. [29] D. Yang, M. Xu, and W. Wu. Feature selection method based on orthogonal experiments for speech recognition. J Tsinghua Univ (Sci & Tech), 43:79-82, 2003. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27773 | - |
| dc.description.abstract | 在機器學習領域當中，特徵選擇一直以來都是一個重要的課題，尤其以行為辨識(Activity Recognition)而言，我們利用許多不同的感應器來擷取各種大量的資訊，假如能透過特徵選擇的技術來挑選出重要特徵，將有許多好處，例如增快辨識速度、提高辨識準確度等等。本論文提出一個基於正交實驗的特徵選擇法，並以循序收納選擇法(Sequential Forward Selection)為基準，比較並探討此法的優劣與適用性。 | zh_TW |
| dc.description.abstract | Feature selection is an important issue in the problem of machine learning. Especially in the domain of activity recognition, many researchers try to make use of multiple heterogeneous sensors and thus receive a large amount of signals. Many features can be extracted, hence feature selection becomes more important. In this thesis, we propose a feature selection method based on orthogonal experimental design and compare this method with equential forward feature selection in terms of the accuracy of the model induced by the selected feature subset versus the number of treatments and the number of selected features. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-12T18:19:49Z (GMT). No. of bitstreams: 1 ntu-96-R94922089-1.pdf: 624887 bytes, checksum: 1e497c5a7ba1cf7bd85b963517d175b3 (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | Acknowledgments ii
Abstract iii List of Figures ix List of Tables xi Chapter 1 Introduction 1 1.1 Motivation . . .1 1.2 The Goal of Feature Selection . . . 2 1.3 Challenge of Feature Selection . . . 2 1.4 Problem Definition . . . 2 1.5 Thesis Organization . . . 4 Chapter 2 Related Work . . . 5 2.1 Activity Recognition . . . 5 2.1.1 RFID Technology . . . 6 2.1.2 Accelerometer Technology . . . 7 2.1.3 Audio/Video Stream . . . 7 2.1.4 Heterogeneous Sensors . . . 8 2.2 Feature Selection Strategies . . . 8 2.2.1 Filter Approaches . . . 10 2.2.2 Wrapper approaches . . . 11 Chapter 3 Preliminaries . . . 15 3.1 Basic concept of Orthogonal Experimental Design . . . 16 3.2 Orthogonal Arrays . . . 19 3.2.1 The Definition and Some Properties of Orthogonal Arrays . . . 20 3.2.2 Constructions for Orthogonal Arrays . . . 22 3.3 Factor Analysis . . . 29 Chapter 4 Feature Selection . . . 33 4.1 The Wrapper Approach for Feature Selection . . . 34 4.2 Feature Selection Based on Orthogonal Experimental Design . . . 37 4.2.1 Two Properties . . . 37 4.2.2 Strength . . . 40 4.2.3 Complexity Analysis . . . 40 4.3 Feature Selection Based on Iterative Orthogonal Experimental Design . . . 41 4.3.1 The Algorithm . . . 41 4.3.2 Complexity Analysis . . . 44 Chapter 5 Experiment . . . 47 5.1 Data Description . . . 47 5.1.1 3-bit Parity Dataset . . . 47 5.1.2 Audio Data of the ADLs Dataset . . . 48 5.1.3 Emotion Dataset . . . 50 5.1.4 The Madelon Dataset of NIPS Competition . . . 50 5.2 Evaluation and Experimental Results . . . 51 5.2.1 3-bit Parity Dataset . . . 51 5.2.2 Real World Datasets . . . 52 Chapter 6 Conclusion . . . 61 Bibliography . . . 63 | |
| dc.language.iso | en | |
| dc.title | 機器學習之特徵選擇-基於正交實驗的探討 | zh_TW |
| dc.title | Feature Selection Based on Iterative Orthogonal Experimental Design | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 許鈞南(Chun-Nan Hsu),王傑智(Chieh-Chih Wang) | |
| dc.subject.keyword | 機器學習,特徵選擇,正交實驗, | zh_TW |
| dc.subject.keyword | Feature Selection,Orthogonal Experimental Design, | en |
| dc.relation.page | 66 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-08-24 | |
| 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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