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Author: 高昶易
Chang-Yi Kao
Thesis Title: 應用於人機介面的多階段學習框架
A Multi-Stage Learning Framework for Human Computer Interface Applications
Advisor: 范欽雄
Chin-Shyurng Fahn
Committee: 古鴻炎
none
戴碧如
none
陳凱瀛
none
鄭瑞恆
none
莊庭瑞
none
Degree: 博士
Doctor
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2013
Graduation Academic Year: 101
Language: 英文
Pages: 198
Keywords (in Chinese): 語音特徵值手勢辨識成本敏感人機介面安全庫存管理
Keywords (in other languages): and Vendor Managed Inventory, Native Voice Eigenvalue, Gesture Recognition, Cost-Sensitive, Human-Computer Interface
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隨著資訊發達以及介面易用性的觀念逐漸普及化,人與電腦、資訊裝置、新興消費性電子等的互動逐漸被重視。最被大家所熟悉的就是蘋果電腦(Apple)積極在人機介面的互動創新,因此在iPod、iPhon等產品上,均提出新穎性的互動作法,而SIRI行動助理就是一個典型應用機器學習的人機介面(HCI, Human-Computer Interface)的應用。學習的技術運用在許多的領域,如:人臉辨識、語者辨識、商品推薦系統等等,現有學習方式都是在大量資料中進行運算。本研究提出一個改良的學習排序演算法:除了多階段的作法,兼具(1).考量到成本敏感(Cost-Sensitive)的議題,先分類收斂到小額資料量、並以Boosting演算法可較快速到達排序功能。(2).參考方向一致性(Concordant/Discordant)作法,改善原本二元分類的模式,並計算出排名之間的距離,用以收斂出最適合之前幾筆少數資料量的排序清單。因此本研究研究所提出的方法改善了上述兩個重要地方。就本研究所提出的方法進行實驗,本研究方法在P@n、MAP以及NDCG三項指標皆略優於其它典型方法。本論文也依據所提出多階段適應學習架構,研究成果分別於三項不同領域進行實驗。
首先本研究應用多階段適應學習架構於手勢辨識。第二部份本研究將應用在語者辨識,以語音訊號轉換成語音特徵值後的原生資料進行比對辨識,而本研究中並非專注於數位訊號處理(DSP, Digital Signal Processing)技術的改善。最後,本研究以物聯網(IoT, Internet of Thing)架構的連結器業者為例,提出一個平衡生產線廠區生產力的架構雛型,並且以網頁作人機介面作為上下游業者拉式溝通的模式,並用本方法實驗以安全庫存為基準進行管理訂單派送。


As information technologies advance and user-friendly interfaces develop, the interaction between humans and computers, information devices, and new consumer electronics is increasingly gaining attention. One example that most people can relate to is Apple’s innovation in HCI (Human-Computer Interface) which has been used on many products such as iPad and iPhone. Siri, the intelligent personal assistant, is a typical application of machine-learning Human-Computer Interface.
Algorithms in machine learning have been employed in many disciplines, including gesture recognition, speaker recognition, and product recommendation systems. While the existing learning algorithms compute and learn from a large quantity of data. In this study, in addition to ranking data through multiple stages, algorithm significantly improves the existing algorithms in two ways. Firstly, it considers the cost-sensitive issue in the ranking algorithm. It classifies and filters data to small quantities and applies the Boosting algorithm to achieve faster ranking performance. Secondly, it enhances the original binary classification by using the concordant and discordant. Results from experiments demonstrate that our proposed algorithm outperforms the conventional methods in three evaluation measures: P@n, MAP, and NDCG. We have also proved in applications in three different areas.
The proposed method was applied to three areas. The experimental results of hand gesture recognition reveal that the efficiency of system execution turns out to be satisfying and the suggested method is desired for application in hand gesture recognition. As for the outcome of average accuracy rate of gesture recognition is more than 98%, a rate of satisfactory. We do not deal the technology improvement with the DSP (Digital Signal Processing). We only process the voice signal converted to the native voice eigenvalue which used to voice recognize. The experiments of the speech recognition show that the recognition optimization procedures established by this study are able to increase the recognition rate to over 96% in the personal computing device and industrial personal computer. It is expected that in the future this voice management system will accurately and effectively identify speakers answering the voice response questionnaire and will successfully carry out the functions in the choice of answers, paying the way for the formation of a virtual customer service person. Finally, we use the Web as the Human-Computer Interface to implement and manage the orders delivered by proposed method. The proposed method in the VMI (Vendor Managed Inventory) system framework achieves an order fulfillment rate 99%, up from the previous 94.75%, or an increase of 4.25% in our experiment result on connector industry. The system is also expected to improve the production efficiency and global competitiveness of the said connector maker.

中文摘要iv Abstractv Abstract Remarksvii 致謝x Contentsxi List of Figuresxvi List of Tablesxx Chapter 1 Introduction1 1.1 Overview1 1.2 Background and motivation1 1.2.1 HCI (Human-Computer Interface)1 1.2.2 Hand gesture recognition5 1.2.3 Speech recognition7 1.2.4 VMI application system8 1.3 Motivation12 1.4 Ph.D. dissertation organization13 Chapter 2 Related Works15 2.1 Gesture recognition17 2.1.1 Face detection and tracking17 2.1.2 Hand detection and tracking19 2.1.3 Hand gesture recognition20 2.2 Speaker recognition22 2.3 The Vendor Managed Inventory application in business26 2.4 The learning method29 2.4.1 Point-wise34 2.4.2 Pair-wise35 2.4.3 List-wise37 2.4 Section summary39 Chapter 3 Proposed Method41 3.1 System algorithm structure41 3.2 Ranking models45 3.2.1 Binary classifier45 3.2.1.1 Support vector machine model47 3.2.1.2 Linear support vector machines48 3.2.1.3 Non-linear support vector machines53 3.2.1.4 The SVM-based multi-classifier56 3.2.2 Boosting introduction58 3.2.2.1 Boosting and machine learning59 3.2.2.2 The Boosting algorithm60 3.3 Boosting schema61 3.3.1 AdaBoost61 3.3.2 The weak classifier68 3.3.3 The AdaBoost-base multi-classifier70 3.4 MultiStageBoost72 3.6 Section summary80 Chapter 4 Proposed Method Experimental Results82 4.1 Performance evaluation82 4.1.1 Precision at position n (P@n)83 4.1.2 Mean Average Precision83 4.1.3 Normalized Discount Cumulative Gain83 4.2 Experimental source data84 4.3 NP200485 4.4 HP200488 Chapter 5 Case Study92 5.1 Exploiting MultiStageBoost model and trajectory of hand motion for hand gesture recognition92 5.1.1 Introduction92 5.1.2 Hand detection and tracking93 5.1.2.1 Skin detection95 5.1.2.2 Face detection96 5.1.2.3 Hand detection97 5.1.2.4 Hand tracking98 5.1.3 Hand gesture recognition100 5.1.3.1 Feature extraction101 5.1.3.2 Gesture definition101 5.1.4 Exploiting proposed method procedure103 5.1.5 Experimental results106 5.1.6 Section summary109 5.2 Application for interactive voice system111 5.2.1 Introduction111 5.2.2 Recognition concept112 5.2.3 Identification model113 5.2.4 A concept of the analysis model120 5.2.5 Experimental results125 5.2.6 Section summary128 5.3 Exploit sensing data to analysis to achieve order arrangement, management and tracking process system130 5.3.1 Introduction130 5.3.2 Operation strategy and goal of connector industry132 5.3.2.1 Short-term goal132 5.3.2.2 Medium-term goal133 5.3.2.3 Long-term goal134 5.3.3 Optimization of value chain134 5.3.4 A case: PHILIPS’ major supplier of connectors136 5.3.5 A design of manufacturing order arrangement and management expert system138 5.3.6 Framework of manufacturing order arrangement order management expert system140 5.3.6.1 Subsystem 1 - M2MICT system141 5.3.6.2 Subsystem 2 - pull model based inventory demand system144 5.3.6.3 Subsystem 3 - manufacturing order arrangement and management service and production transparency systems146 5.3.7 Self- learning model149 5.3.7 Introduction of system and analysis of benefits156 5.3.7.1 Operation process before introduction of system (As-Is)156 5.3.7.2 Operation process after introduction of system (To-Be)157 5.3.8 Section summary159 Chapter 6 Conclusions and Future Works162 6.1 Conclusion162 6.2 Future works164 References165 Appendix176 作者簡介177

[1] Fenn, J., “Gartner’s hype cycle special report for 2011”, Technical Report, Gartner, Stamford, Connecticut (2011)
[2] Fenn, J., Gammage, B., and Raskino, M., “Gartner’s hype cycle special report for 2010”, Technical Report, Gartner, Stamford, Connecticut (2010)
[3]Yu, L., Zhang, D., and Wang, K., “The relative distance of key point based iris recognition”, Pattern Recognition, Vol. 40, No.2, pp. 423-430 (2007)
[4]Oka, K., Sato, Y., and Koike, H., “Real-time fingertip tracking and gesture recognition”, Computer Graphics and Applications, Vol. 22, No.6, pp. 64-71 (2002)
[5]Wang, K. Y., “A real-time face tracking and recognition system based on particle filtering and AdaBoosting techniques”, Master Thesis, National Taiwan University of Science and Technology, Taipei, Taiwan (2006)
[6]Bradski, G. R., “Computer vision face tracking for use in a perceptual user interface”, Intel Technology Journal, Vol. 2, No. 2, pp. 1-15 (1998)
[7]Shan, C., Wei, Y., Tan, T., and Ojardias, F., “Real-time hand tracking by combining particle filtering and mean shift”, Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 669-674 (2004)
[8]Song, K. T. and Chen, W. J., “Face recognition and tracking for human-robot interaction”, Proceedings of the 2004 IEEE International Conference on Systems, Man, and Cybernetics, Hague, Netherlands, Vol.3, pp. 2877-2882 (2004)
[9]Liu, X. and Fujimura, K., “Hand gesture recognition using depth data”, Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 529-534 (2004)
[10]Jiang, H., Li, Z. N., and Drew, M. S., “Human posture recognition with convex programming”, Proceedings of the 2005 IEEE International Conference on Multimedia and Expo, Amsterdam, Netherlands, pp. 574-577 (2005)
[11]Soontranon, N., Aramith, S., and Chalidabhongse, T. H., “Improved face and hand tracking for sign language recognition”, Proceedings of the 2005 IEEE International Conference on Information Technology: Coding and Computing, Bangkok, Thailand, Vol. 2, pp. 141-146 (2005)
[12]Zhu, X., Yang, J., and Waibel, A., “Segmenting hands of arbitrary color”, Proceedings of the 2000 IEEE International Conference on Automatic Face and Gesture Recognition, Pittsburgh, Pennsylvania, pp. 446-453 (2000)
[13]Mitra, S. and Acharya, T., “Gesture recognition: a survey”, Proceedings of the IEEE Transactions on Systems, Kolkata, India, Vol. 37, No. 3, pp. 311-324 (2007)
[14]Liu, N., Lovell, B. C., Kootsookos, P. J., and Davis, R. I. A., “Model structure selection and training algorithm for a HMM gesture recognition system”, Proceedings of the International Workshop in Frontiers of Handwriting Recognition, Brisbane, Australia, pp. 100-106 (2004)
[15]Yang, M. H., Kriegman, D., and Ahuja, N., “Detecting faces in images: a survey”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pp. 34-58 (2002)
[16]Yang, G. and Huang, T. S., “Human face detection in complex background”, Pattern Recognition, Vol. 27, No. 1, pp. 53-64 (1994)
[17]Yow, K. C. and Cipolla, R., “Feature-based human face detection”, Image and Vision Computing, Vol. 15, No. 9, pp. 712-735 (1997)
[18]Chai, D. and Bouzerdoum, A., “A Bayesian approach to skin color classification in YCbCr color space”, Proceedings of the 2000 IEEE Region Ten Conference, Kuala Lumpur, Malaysia, pp. 421-424 (2000)
[19]Lanitis, A., Taylor, C. J., and Cootes, T. F., “An automatic face identification system using flexible appearance models”, Image and Vision Computing, Vol. 13, No. 5, pp. 393-401 (1995)
[20]Vaillant, R., Monrocq, C., and Cun, Y. L., “An original approach for the localization of objects in images”, Proceedings of the IEEE Conference on Artificial Neural Networks, Brighton, England, pp. 26-30 (1993)
[21]Turk, M. and Pentland, A., “Eigenfaces for recognition”, Journal of Cognitive Neuroscence, Vol. 3, No. 1, pp. 71-86 (1991)
[22]An, K. H., Yoo, D. H., Jung, S. U., and Chung, M. J., “Robust multi-view face tracking”, Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Edmonton, Canada, pp. 1905-1910 (2005)
[23]Vacavant, A. and Chateau, T., “Real-time head and hands tracking by monocular vision”, Proceedings of the 2005 IEEE International Conference on Image Processing, Genoa, Italy, pp. 11-14 (2005)
[24]Kim, K. K., Kwak, K. C., and Chi, S. Y., “Gesture analysis for human-robot interaction”, Proceedings of the 8th International Conference on Advanced Communication Technology, Vol. 3, pp. 20-22 (2006)
[25] Elmezain, M., Al-Hamadi, A., and Michaelis, B., “Real-time capable system for hand gesture recognition using hidden Markov models in stereo color image sequences”, The Journal of Winter School of Computer Graphics, Vol. 16, No. 1, pp. 65-72 (2008)
[26]Liang, R. H. and Ouhyoung, M., “A real-time continuous gesture recognition system for sign language”, Proceedings of the IEEE International Conference on Auto Face and Gesture Recognition, Taipei, Taiwan, pp. 558-867 (1998)
[27]Wilson, A. and Oliver, N., “GWINDOWS: towards robust perception-based UI”, Proceedings of the Computer Vision and Pattern Recognition, Madison, Wisconsin, pp. 46 (2003)
[28] Jonsson, K., Matas, J., Kittler, J., and Li, Y., “Learning support vectors for face verification and recognition”, Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, Guildford, England, pp. 208-213 (2000)
[29] Moriyama, T., Kanade, T., Xiao, J., and Cohn, J. F., “Meticulously detailed eye region model and its application to analysis of facial images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 5, pp. 738-752 (2006)
[30] Shan, C., Gong, S., and McOwan, P. W., “Fusing gait and face cues for human gender recognition”, Neurocomputing, Vol. 71, No. 10, pp. 1931-1938 (2008)
[31] Balci, K. and Atalay, V., “PCA for gender estimation: which eigenvectors contribute”, Proceedings of the 16th International Conference on Pattern Recognition, Quebec, Canada, pp. 363-366 (2002)
[32] Iga, R., Izumi, K., and Hayashi, H., “A gender and age estimation system from face images”, Proceedings of the Society of Instrument and Control Engineers Annual Conference, Fukui, Japan, pp. 756-761 (2003)
[33] Flin, R. H., “Age effect in children’s memory for unfamiliar faces”, Developmental Psychology, Vol. 16, No. 4, pp. 373-374 (1980)
[34] Hobson, R. P., “The autistic children’s recognition of age-and-sex-related characteristics of people”, Journal of Autism and Developmental Disorders, Vol. 17, No. 1, pp. 63-79 (1997)
[35] Fahn, C. S., Wu, H. M., and Kao, C. Y., “Real-time facial expression recognition in image sequences using an AdaBoost-based multi-classifier”, Proceedings of Asia-Pacific Signal and Information Processing Association 2009 on 3D Synthesis and Expression, Sapporo, Japan (2009)
[36] Joseph, P. and Campbell, J. R.., “Speaker recognition: a tutorial”, Proceedings of the IEEE, Vol. 85, No. 9, pp. 1437-1462 (1997)
[37] Wutiwiwatchai, C., Achariyakulporn, V., and Tanprasert, C., “Text-dependent speaker identification using LPC and DTW for Thai language”, Proceedings of the IEEE Region 10 Conference, Cheju Island, Korea, pp. 674-677 (1999)
[38] Lee, H. R., Chen, C., and Roger, J. S., “Approximate lower-bounding functions for the speedup of DTW for melody recognition”, Proceedings of the 9th IEEE International Workshop on Cellular Neural Networks and their Applications, Hsinchu, Taiwan, pp. 178-181 (2005)
[39] Roger, J. S., Hsu, C. L., and Lee, H. R., “Continuous HMM and its enhancement for singing/humming query retrieval”, Proceedings of the International Symposium on Music Information Retrieval, London, England, pp. 546-551 (2005)
[40] McLeod, P. and Wyvill, G., “A smarter way to find pitch”, Proceedings of the International Computer Music Conference, Leith Walk, New Zealand (2005).
[41] Porter, M. E., Competitive in Global Industries, Harvard Business School Press, Boston, Massachusetts (1986)
[42] Porter, M. E., Competitive Strategy: Techniques for Analyzing Industries and Competitors, New York: Free Press, New York, New York (1980)
[43] Sohal, A. S., Keller, A. Z., and Fouad, R. H., “A review of literature relating to JIT”, International Journal of Production and Management, Vol. 9 , No. 3, pp. 15-25 (1988)
[44] Willis, T. and Huston, C., “Vendor requirements and evaluation in a JIT environment”, International Journal of Operations and Production Management, Vol. 10, No. 4, pp. 41-50 (1990)
[45] Lawrence, J. J. and Lewis, H. S., “Understanding the use of just-in-time purchasing in a developing country: the case of Mexico”, International Journal of Operations and Production Management, Vol. 16, No. 6, pp. 68-80 (1996)
[46] Schonberger, R. J. and Gilbert, J. P., “Just-in-time purchasing: A challenge for U.S. industry”, California Management Review, Vol. 26, No. 1, pp. 54-68 (1983)
[47] Nikhil, S., “CPFR at Whirlpool corporation: two heads and an exception engine”, The Journal of Business Forecasting Methods and Systems, Vol. 22, No. 4, pp. 3-8 (2003)
[48] Simchi, L. D. and Kaminsky, P., Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies, 3rd Edition, McGraw Hill, New York, New York (2009)
[49] Steermann, H., “A practical look at CPFR: the Sears-Michelin experience”, Supply Chain Management Review, Vol. 7, No. 4, pp. 46-53 (2003)
[50] Robbins, S. P. and Judge, T. A., Organizational Behavior, 14th Edition, Prentice Hall, Bergen County, New Jersey (2011)
[51] Venkatraman, N. and Ramanujam, V., “Measurement of business performance in strategy research: a comparison of approaches”, Academy of Management Review, Vol. 11, No. 4, pp. 801-815 (1986)
[52] Coomans, D. and Massart, D. L., “Alternative K-nearest neighbors’ rules in supervised pattern recognition: Part 1. K-nearest neighbors’ classification by using alternative voting rules”, Analytica Chimica Acta, Vol. 136, No. 1, pp. 15-27 (1982)
[53] Hartigan, J. A. and Wong, M. A., “A K-means clustering algorithm”, Applied Statistics, Vol. 28, No. 1, pp. 100-108 (1979)
[54] MacQueen, J. B., “Some methods for classification and analysis of multivariate observations”, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, California, pp. 281-297 (1967)
[55] Burges, C. J., “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121-167 (1998)
[56] Hsu, C. W. and Lin, C. J., “A comparison of methods for multiclass support vector machines”, IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 415-425 (2002)
[57] Li, P., Burges, J. C., and Wu, Q., “McRank: learning to rank using multiple classification and gradient Boosting”, Neural Information Processing Systems, Vol. 3, No. 1, pp. 897-904 (2007)
[58] Joachims, T., “Optimizing search engines using click through data”, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, New York, New York, pp. 133-142 (2002)
[59] Freund, Y., Iyer, R., Schapire, R.E., and Singer, Y., “An efficient Boosting algorithm for combining preferences”, The Journal of Machine Learning Research, Vol. 4, No. 1, pp. 933-969 (1998)
[60] Tsai, M. F., Liu, T. Y., Qin, T., Chen, H. H., and Ma, W. Y., “FRank: a ranking method with fidelity loss”, Proceedings of the 30th annual international ACM Special Interest Groups for International Retrieval conference on Research and development in information retrieval, Amsterdam, Netherlands, pp. 383-390 (2007)
[61] Cao, Z., Qin, T., Liu, T. Y., Tsai, M. F., and Li, H., “Learning to rank: from pair-wise approach to list-wise approach”, Proceedings of the 24th international conference on Machine learning, Corvallis, Oregon, pp. 129-136 (2007)
[62] Liu, T. Y., “Learning to rank for information retrieval”, Information Retrieval, Vol. 3, No. 3, pp. 225-331 (2008)
[63] Herbrich, R., Graepel, T., and Obermayer, K., Large Margin Ranks Boundaries for Ordinal Regression, MIT Press, Cambridge, Massachusetts (2000)
[64] Freund, Y., Iyer, R. D., Schapire, R. E., and Singer, Y., “An efficient Boosting algorithm for combining preferences”, Journal of Machine Learning Research, Vol. 4, No. 1, pp. 933-969 (2003)
[65] Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., and Ullender, G., “Learning to rank using gradient descent”, Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, pp. 89-96 (2005)
[66] Yang, M. H., Kriegman, D., and Ahuja, N., “Detecting faces in images: a survey”, IEEE Transaction on Pattern Anal. Machine Intelligent, Vol. 24, No.1, pp. 34-58 (2002)
[67] Xu, J. and Li, H., “AdaRank: a Boosting algorithm for information retrieval”, Proceedings of the 30th annual international ACM Special Interest Group on Information Retrieval conference on Research and development in information retrieval, Amsterdam, Netherlands, pp. 391-398 (2007)
[68] Liu, X. Y., “Study on agency, outsourcing and in-house production for Taiwan connector industry: case on a company”, Master Thesis, National Chengchi University, Taipei, Taiwan (2007)
[69] Chien, Y. T., “A study of innovation Strategy and operating performance and related factors of Taiwan electronic connector industry”, Master Thesis, Shih Hsin University, Taipei, Taiwan (2007)
[70] Ludmila, I. K. and Christopher, J. W., “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy”, Machine Learning, Vol. 51, No. 2, pp. 181-207 (2003)
[71] Keerthi, S. S. and Lin C. J., “Asymptotic behaviors of support vector machines with Gaussian kernel”, Neural Computation, Vol. 15, No. 7, pp. 1667-1689 (2003)
[72] Hersh, W., Buckley, C., Leone, T. J., and Hickam, D., “OHSUMED: an interactive retrieval evaluation and new large test collection for research”, Proceedings of the 17th annual international ACM Special Interest Group on Information Retrieval conference on Research and development in information retrieval, Dublin, Ireland, pp. 192-201 (1994)
[73] Wu, H., Siegel, M., Stiefelhagen, R., and Yang, J., “Sensor fusion using Dempster-Shafer theory [for context-aware HCI]”, Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference, Pittsburgh, Pennsylvania, pp. 7-12 (2002)
[74] Yang, S. L., Li, Y. S., Hu, X. X., and PAN, R. Y., “Optimization study on k value of K-means algorithm”, Systems Engineering-Theory and Practice, Vol. 26, No. 2, pp. 97-101 (2006)
[75] Kendall, M. G. and Gibbons, J. D., Rank Correlation Methods, Hafner, New York (1955)
[76] Freund, Y. and Schapire, R. E., “Experiments with a new Boosting algorithm”, Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, pp. 148-156 (1996)
[77] Friedman, J., Hastie, T., and Tibshirani, R., “Additive logistic regression: a statistical view of Boosting”, The Annals of Statistics, Vol. 28, No. 2, pp. 337-407 (2000)
[78] Schapire, R. E. and Singer, Y., “Improved Boosting algorithms using confidence-rated predictions”, Machine Learning, Vol. 37, No. 3, pp. 297-336 (1999)
[79] Liu, T. Y., Qin, T., Xu, J., Xiong, W., and Li, H., “LETOR: a benchmark collection for research on learning to rank for information retrieval”, Information Retrieval, Vol. 13, No. 4, pp. 346-374 (2009)
[80] Halkidi, M., Batistakis, Y., and Vazirgiannis, M., “Clustering validity checking methods: part II”, The ACM Special Interest Group on Management of Data Record, Vol. 31, No. 3, pp. 19-27 (2002)
[81] Davies, D. L. and Bouldin, D. W., “A cluster separation measure”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 1, No. 2, pp. 224-227 (1979)

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