Classification using distance nearest neighbours

Statistics and Computing

Volumn 21, Issue 3, 2011, Pages 431-437

  Friel, N ^a   Pettitt, A N ^b  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

^b QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

Classification; k nearest neighbours; Markov chain Monte Carlo

Indexed keywords

EID: 79958228578     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-010-9179-y     Document Type: Article

References (13)

1. Besag, J. E.: Spatial interaction and the statistical analysis of lattice systems (with discussion). J. R. Stat. Soc., Ser. B 36, 192-236 (1974).
2. Cucala, L., Marin, J.-M., Robert, C. P., Titterington, D. M. T.: A Bayesian reassessment of nearest-neighbour classification. J. Am. Stat. Assoc. 104, 263-273 (2009).
3. Dean, N., Murphy, T. B., Downey, G.: Using unlabelled data to update classification rules with applications in food authenticity studies. Appl. Stat. 55, 1-14 (2006).
4. Dudani, S. A.: The distance-weighted k-nearest neighbor rule. IEEE Trans. Syst. Man. Cyber. SMC-6, 325-327 (1976).
5. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Springer, Berlin (2001).
6. Holmes, C. C., Adams, N. M.: A probabilistic nearest-neighbour algorithm for statistical pattern recognition. J. R. Stat. Soc., Ser. B 64, 295-306 (2002).
7. Holmes, C. C., Adams, N. M.: Likelihood inference in nearest-neighbour classification models. Biometrika 90, 99-112 (2003).
8. McElhinney, J., Downey, G., Fearn, T.: Chemometric processing of visible and near infrared reflectance spectra for species identification in selected raw homogenised meats. J. Near Infrared Spectrosc. 7, 145-154 (1999).
9. Møller, J., Pettitt, A. N., Reeves, R., Berthelsen, K. K.: An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika 93, 451-458 (2006).
10. Murray, I., Ghahramani, Z., MacKay, D.: MCMC for doubly-intractable distributions. In: Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-06). AUAI Press, Arlington (2006).
11. Propp, J. G., Wilson, D. B.: Exactly sampling with coupled Markov chains and applications to statistical mechanics. Random Struct. Algorithms 9, 223-252 (1996).
12. Sisson, S., Fan, Y., Tanaka, M.: Sequential Monte Carlo without likelihoods. Proc. Nat. Acad. Sci. USA 104(6), 1760-1765 (2007).
13. Zhu, X., Ghahramani, Z.: Towards semi-supervised classification with Markov random fields. Technical Report CMU-CALD-02-106. Carnegie Mellon University (2002).