Classification using distance nearest neighbours

  title={Classification using distance nearest neighbours},
  author={N. Friel and A. Pettitt},
  journal={Statistics and Computing},
  • N. Friel, A. Pettitt
  • Published 2011
  • Mathematics, Computer Science
  • Statistics and Computing
  • This paper proposes a new probabilistic classification algorithm using a Markov random field approach. The joint distribution of class labels is explicitly modelled using the distances between feature vectors. Intuitively, a class label should depend more on class labels which are closer in the feature space, than those which are further away. Our approach builds on previous work by Holmes and Adams (J. R. Stat. Soc. Ser. B 64:295–306, 2002; Biometrika 90:99–112, 2003) and Cucala et al. (J. Am… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Spatial hidden Markov models and species distributions
    • 1
    Pre-processing for approximate Bayesian computation in image analysis
    • 28
    • PDF
    Evidence and Bayes Factor Estimation for Gibbs Random Fields
    • 19
    • PDF
    Bayesian Inference on a Mixture Model With Spatial Dependence
    • 16
    • PDF
    Chemometrics in studies of food origin
    • 7


    Publications referenced by this paper.
    A probabilistic nearest neighbour method for statistical pattern recognition
    • 133
    • Highly Influential
    • PDF
    Likelihood inference in nearest‐neighbour classification models
    • 28
    • PDF
    A Bayesian Reassessment of Nearest-Neighbor Classification
    • 48
    • Highly Influential
    • PDF
    The Distance-Weighted k-Nearest-Neighbor Rule
    • 1,007
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
    • 13,039
    • PDF
    An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
    • 314
    • PDF
    Exact sampling with coupled Markov chains and applications to statistical mechanics
    • 841
    MCMC for Doubly-intractable Distributions
    • 312
    • PDF