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Recognition Problem of Biometrics: Nonparametric Dependence Measures and Aggregated Algorithms

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Statistical Methods in Counterterrorism
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References

  1. Bazaraa, M. S., J. J. Jarvis, and H. D. Sherali. 1990. Linear programming and network flows. New York: Wiley.

    MATH  Google Scholar 

  2. Breiman, L. 2004. “Population theory for boosting ensembles.” Annals of Statistics 32:1–11.

    Article  MathSciNet  Google Scholar 

  3. Critchlow, D. E. 1985. Metric methods for analyzing partially ranked data. New York: Springer.

    MATH  Google Scholar 

  4. Diaconis, P. 1988. Group representations in probability and statistics. Hayward, CA: Institute of Mathematical Statistics.

    MATH  Google Scholar 

  5. Diaconis, P., and R. L. Graham. 1997. “Spearman’s footrule as a measure of disarray.” Journal of the Royal Statistical Society Series B 39:262–268.

    MathSciNet  Google Scholar 

  6. Genest, C., K. Ghoudy, and L. P. Rivest. 1995. “A semiparametric estimation procedure of dependence parameters in multivariate families of distributions.” Biometrika 82:543–552.

    Article  MathSciNet  Google Scholar 

  7. Jain, A. K., R. Bolle, and S. Pankanti. 1999. Personal identification in networked society. Dordrecht: Kluwer.

    Google Scholar 

  8. Jain, A. K., R. P. W. Duin, and J. Mao. 2000. “Statistical pattern recognition: A review.” IEEE Transactions on Pattern Analysis and Machine Intelligence 22:4–37.

    Article  Google Scholar 

  9. Joe, H. 1990. Multivariate models and dependence. London: Chapman & Hall.

    Google Scholar 

  10. Kittler, J., M. Hatef, R. P. W. Duin, and J. Matas. 1998. “On combining classifiers.” IEEE Transactions on Pattern Analysis and Machine Intelligence 20:66–75.

    Article  Google Scholar 

  11. Marden, J. I. 1995. Analyzing and modeling rank data. London: Chapman & Hall.

    MATH  Google Scholar 

  12. Marley, A. A. M. 1993. “Aggregation theorems and the combination of probabilistic rank orders.” In Probability models and statistical analyses for ranking data, edited by M. A. Fligner and J. S. Verducci, Volume 80 of Lecture Notes in Statistics, 245–272. New York: Springer.

    Google Scholar 

  13. Nelsen, R. 1999. An introduction to copulas. Volume 139 of Lecture Notes in Statistics. New York: Springer.

    MATH  Google Scholar 

  14. Phillips, P. J., H. Moon, S. A. Rizvi, and P. J. Rauss. 2000. “The FERET evaluation methodology for face-recognition algorithms.” IEEE Transactions on Pattern Analysis and Machine Intelligence 22:1090–1104.

    Article  Google Scholar 

  15. Rukhin, A. L., and I. Malioutov. 2005. “Fusion of algorithms via weighted averaging of ranks.” Pattern Recognition Letters 26:679–684.

    Article  Google Scholar 

  16. Rukhin, A. L., and A. Osmoukhina. 2005. “Nonparametric measures of dependence for biometric data studies.” Journal of Statistical Planning and Inference 131:1–18.

    Article  MathSciNet  Google Scholar 

  17. Schapire, R. E., Y. Freund, P. Bartlett, and W. S. Lee. 1998. “Boosting the margin: a new explanation for the effectiveness of voting methods.” Annals of Statistics 26:1651–1686.

    Article  MathSciNet  Google Scholar 

  18. Stern, H. 1993. “Probability models on rankings and the electoral process.” In Probability models and statistical analyses for ranking data, edited by M. A. Fligner and J. S. Verducci, Volume 80 of Lecture Notes in Statistics. New York: Springer.

    Google Scholar 

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Rukhin, A.L. (2006). Recognition Problem of Biometrics: Nonparametric Dependence Measures and Aggregated Algorithms. In: Wilson, A.G., Wilson, G.D., Olwell, D.H. (eds) Statistical Methods in Counterterrorism. Springer, New York, NY. https://doi.org/10.1007/0-387-35209-0_6

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