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Estimating the Posterior Probabilities Using the K-Nearest Neighbor Rule
 

Summary: Estimating the Posterior Probabilities
Using the K-Nearest Neighbor Rule
Amir F. Atiya
Dept Computer Engineering
Cairo University
Giza, Egypt
amir@alumni.caltech.edu
July 19, 2004
Abstract
In many pattern classification problems an estimate of the posterior prob-
abilities (rather than only a classification) is required. This is usually the case
when some confidence measure in the classification is needed. In this paper
we propose a new posterior probability estimator. The proposed estimator
considers the K-nearest neighbors. It attaches a weight to each neighbor that
contributes in an additive fashion to the posterior probability estimate. The
weights corresponding to the K-nearest-neighbors (which add to 1) are esti-
mated from the data using a maximum likelihood approach. Simulation studies
confirm the effectiveness of the proposed estimator.
1 Introduction
The posterior probability a key variable in any pattern classification problem. For

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences