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Global Reweighting and Weight Vector Based Strategy for Multiclass Boosting

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Neural Information Processing (ICONIP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7664))

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Abstract

Boosting is a generic statistical process for generating accurate classifier ensembles from moderately accurate learning algorithm. This paper presents a new generic boosting style procedure, M-Boost, for learning multiclass concepts. M-Boost uses a global strategy for selecting the weak classifier, a global weight reassignment strategy, a vector valued weight for the selected classifiers, and an ensemble that outputs a probability distribution on classes.

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References

  1. Valiant, L.G.: A Theory of the Learnable. Communications of ACM 9 (1984)

    Google Scholar 

  2. Kearns, M., Valiant, L.G.: Cryptographic Limitations on Learning Boolean Formula and Finite Automata. In: Precedings of Twenty-First Annual ACM Symposium on Theory of Computing (1989)

    Google Scholar 

  3. Schapire, R.E.: Strength of weak learnability. J. Machine Learning 5(2), 197–227 (1990)

    Google Scholar 

  4. Freund, Y.: Boosting a Weak Learning Algorithm by Majority Vote. Information and Computation 121(2), 256–285 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  5. Freund, Y., Schapire, R.E.: A Decision Theoretic Generlization of Online Learning. Journal of Computer and System Sciences 55(1), 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  6. Schapire, R.E., Singer, Y.: Improved Boosting algorithm using confidence rated predictions. Machine Learning 37(3), 297–336 (1999)

    Article  MATH  Google Scholar 

  7. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)

    MATH  Google Scholar 

  8. Schapire, R.E.: Using output codes to boost multiclass learning problems. In: Machine Learning: Proceedings of Fourth International Conference (1997)

    Google Scholar 

  9. Zhu, J., Zou, H., Saharon, R., Hastie, T.: Multi-class AdaBoost. Statistics and Its Interface 2, 349–360 (2009)

    MathSciNet  MATH  Google Scholar 

  10. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth Inc. (1984)

    Google Scholar 

  11. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting. The Annals of Statistics 38(2), 337–374 (2000)

    Article  MathSciNet  Google Scholar 

  12. Frank, A., Asuncion, A.: UCI Machine Learning Repository. School of Information and Computer Science. University of California, Irvine (2010), http://archive.ics.uci.edu/ml

    Google Scholar 

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Author information

Authors and Affiliations

  1. LUMS School of Science and Engineering Lahore, Pakistan

    M. Baig & Mian Muhammad Awais

Authors
  1. M. Baig
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  2. Mian Muhammad Awais
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Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Education City, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, Hubei, China

    Zhigang Zeng

  3. College of Computer Science, Chongqing University, 174 Shazhengjie Street, 400044, Chongqing, China

    Chuandong Li

  4. Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Chi Sing Leung

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© 2012 Springer-Verlag Berlin Heidelberg

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Baig, M., Awais, M.M. (2012). Global Reweighting and Weight Vector Based Strategy for Multiclass Boosting. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_55

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  • DOI: https://doi.org/10.1007/978-3-642-34481-7_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34480-0

  • Online ISBN: 978-3-642-34481-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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