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BTW: A New Distance Metric for Classification

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 151))

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Abstract

In this paper, BTW, a new method for similar case search, is presented. The main objective is to optimize the metrics employed in classical approaches in order to obtain an intense compression in the data and a deterministic real-time behavior; and without compromising the performance of the classification task. BTW tries to conjugate the best of three well-known techniques: Nearest Neighbor, Fisher discriminant and optimization.

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References

  1. Michie, D., Spiegelhalter, D.J., Taylor, C.C. (eds.): Machine Learning, Neural and Statistical Classification, Hertfordshire. Ellis Horwood Series in Artificial Intelligence, p. 216

    Google Scholar 

  2. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley, New York (2000)

    Google Scholar 

  3. Cover, T.M.: Rates of convergence for nearest neighbor procedures. In: Proc. of the Hawaii Inter. Conference on System Sciences, pp. 413–415. Western Periodicals, Honolulu (1968)

    Google Scholar 

  4. Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)

    Google Scholar 

  5. Lowe, D.G.: Similarity metric learning for a variable-kernel classifier. Neural Computation 7, 72–85 (1995)

    Article  Google Scholar 

  6. Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 607–616 (1996)

    Article  Google Scholar 

  7. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn., New York. Springer Series in Statistics (2009)

    Google Scholar 

  8. Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood Component Analysis. In: Advances in Neural Information Processing Systems, vol. 17, pp. 513–520 (2005)

    Google Scholar 

  9. Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning a Mahalanobis metric from equivalence constraints. Journal of Machine Learning Research 6(1), 937–965 (2006)

    MathSciNet  Google Scholar 

  10. Weinberger, K.Q., Saul, L.K.: Distance Metric Learning for Large Margin Nearest Neighbor Classification. Journal of Machine Learning Research 10 (2009)

    Google Scholar 

  11. Efron, B., Tibshirani, R.: An Introduction to the Bootstrap. Chapman and Hall, N.York (1996)

    Google Scholar 

  12. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (2010), http://www.ics.uci.edu/~mlearn/MLRepository.html

  13. Friedman, J.: Another Approach to Polychotomous Classification, Internal report (1996)

    Google Scholar 

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Correspondence to Julio Revilla Ocejo .

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© 2012 Springer-Verlag Berlin Heidelberg

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Ocejo, J.R., Bukubiye, E.K. (2012). BTW: A New Distance Metric for Classification. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_84

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  • DOI: https://doi.org/10.1007/978-3-642-28765-7_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28764-0

  • Online ISBN: 978-3-642-28765-7

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