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    題名: 普適提演算法比較
    作者: 陳炳霖
    貢獻者: 余清祥
    張源俊


    陳炳霖
    關鍵詞: 普適提
    日期: 2003
    上傳時間: 2009-09-14
    摘要: 普適提(Boosting)是近年來發展迅速且廣泛被應用於分類問題的方法之一，
    其特點是利用精確度較低或是較粗糙的分類方法(weak learner)為基礎，
    經由多次反覆分類之後，
    將其結果合併而提升分類的精確度。
    本研究為探討若是SVM, KNN, LDA等分類基底時，
    普適提是否仍然能夠達到改進的效果，
    或是會造成模型過適的情況(overfitting)。
    我們發現到普適提在訓練資料集(training set)與測試資料集(testing set)的解釋變數
    獨立且同分佈(independent identically ditributed)產生的情況下，
    無論用任何一種分類法則皆有造成模型過適的可能；
    但是，若訓練資料集與測試資料集的相關程度很高的情況下，
    則不會發生模型過適的情況，
    因此，欲探討不同相關係數以及分布類似的程度對測試資料集的結果有何影響。
    參考文獻: Dettling, M. and Buhlmann, P. (2002) How to use boosting for tumor
    classification with gene expression data.
    Escudero, G. , Marquez, L. and Rigau, G. (2000) Boosting applied to
    word sense disambiguation. In LNAI 1810: Proceedings of the 12th
    European Conference on Machine Learning, ECML, pages 129-141.
    Freund, Y. (2001) An adaptive version of the boost by majority algorithm.
    Machine Learning, 43(3):293-318.
    Freund, Y. and Schapire, R.E. (1999) A short introduction to boosting.
    Journal of Japanese Society for Artificial Intelligence, 14(5):771-
    780.
    Hastie, T., Tibshirani, R. and Friedman, J. (2001) The Elements of
    Statistical Learning: data mining, inference and prediction.
    Lebanon, G. and Lafferty, J. (2001) Boosting and maximum likelihood
    for exponential models. In Neural Information Processing Systems
    (NIPS), volume 14.
    Long, P.M. (2002) Minimum majority classification and boosting. In
    AAAI
    Lugosi, G. and Vayatis, N. (2002) A consistent strategy for boosting
    algorithms. In Proceedings of the Annual Conference on Computational
    Learning Theory, volume 2375 of LNAI, pages 303-318.
    Mannor, S. and Meir, R. (2001) Weak learners and improved convergence
    rate in boosting. In Advances in Neural Information Processing
    Systems 13: Proc.NIPS.
    Mannor, S., Meir, R. and Mendelson, S. (2001) On the consistency of
    boosting algorithms. submitted to Advances in Neural Information
    Processing 14.
    Meir, R. and Ratsch, G. (2003) An introduction to boosting and leveraging.
    In S. Mendelson and A. Smola, editors, Advanced Lectures
    on Machine Learning, LNCS, pages 119-184.
    Onoda, T., Ratsch, G. and Muller, K.-R. (2000) Applying support
    vector machines and boosting to a non-intrusive monitoring system
    for household electric appliances with inverters.
    Ratsch, G., Mika, S. , Scholkopf, B. and Muller, K.-R. (2000) Constructing
    boosting algorithms from SVMs: an application to oneclass
    classification. IEEE PAMI, 24(9).
    Ratsch, G., Scholkopf, B. , Mika, S. and Muller, K.-R. (2000) SVM
    and Boosting: One class. Technical Report 119, GMD FIRST,
    Berlin.
    Ratsch, G. and Warmuth, M.K. (2002) Maximizing the margin with
    boosting. In Proceedings of the Annual Conference on Computational
    Learning Theory, volume 2375 of LNAI, pages 334-350.
    Ratsch, G. andWarmuth, M.W. (2002) Efficient margin maximization
    with boosting.
    Schapire, R.E. (1999) A brief introduction to boosting. In Proceedings
    of the Sixteenth International Joint Conference on Artificial
    Intelligence.
    Yaniv, R.E., Meir, R. and David, S.B. (2000) Localized boosting.
    In Proceedings of the 13th Annual Conference on Computational
    Learning Theory, pages 190-199.
    描述: 碩士
    國立政治大學
    統計研究所
    91354025
    92
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0091354025
    資料類型: thesis
    顯示於類別:[統計學系] 學位論文

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