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  1. International Journal of Machine Learning and Cybernetics
  2. International Journal of Machine Learning and Cybernetics : Volume 4
  3. International Journal of Machine Learning and Cybernetics : Volume 4, Issue 4, August 2013
  4. Probabilistic characterization of nearest neighbor classifier
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International Journal of Machine Learning and Cybernetics : Volume 8
International Journal of Machine Learning and Cybernetics : Volume 7
International Journal of Machine Learning and Cybernetics : Volume 6
International Journal of Machine Learning and Cybernetics : Volume 5
International Journal of Machine Learning and Cybernetics : Volume 4
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 6, December 2013
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 5, October 2013
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 4, August 2013
Probabilistic characterization of nearest neighbor classifier
Investigation of relationship between object-oriented metrics and change proneness
Consistency-preserving attribute reduction in fuzzy rough set framework
Performance of global–local hybrid ensemble versus boosting and bagging ensembles
Self organizing map and wavelet based image compression
Synchronization of competitive neural networks with different time scales and time-varying delay based on delay partitioning approach
Hybrid (fuzzy-stochastic) modelling in construction operations management
System-on-programmable-chip implementation of diminishing learning based pattern recognition system
Implication operators on the set of ∨-irreducible element in the linguistic truth-valued intuitionistic fuzzy lattice
Probabilistic DEAR models
A boundary restricted adaptive particle swarm optimization for data clustering
SIFT based iris recognition with normalization and enhancement
A multi-agent based model for collective purchasing in electronic commerce
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 3, June 2013
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 2, April 2013
International Journal of Machine Learning and Cybernetics : Volume 4, Issue 1, February 2013
International Journal of Machine Learning and Cybernetics : Volume 3
International Journal of Machine Learning and Cybernetics : Volume 2
International Journal of Machine Learning and Cybernetics : Volume 1

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Probabilistic characterization of nearest neighbor classifier

Content Provider SpringerLink
Author Dhurandhar, Amit Dobra, Alin
Copyright Year 2012
Abstract The k-nearest neighbor classification algorithm (kNN) is one of the most simple yet effective classification algorithms in use. It finds major applications in text categorization, outlier detection, handwritten character recognition, fraud detection and in other related areas. Though sound theoretical results exist regarding convergence of the generalization error (GE) of this algorithm to Bayes error, these results are asymptotic in nature. The understanding of the behavior of the kNN algorithm in real world scenarios is limited. In this paper, assuming categorical attributes, we provide a principled way of studying the non-asymptotic behavior of the kNN algorithm. In particular, we derive exact closed form expressions for the moments of the GE for this algorithm. The expressions are functions of the sample, and hence can be computed given any joint probability distribution defined over the input–output space. These expressions can be used as a tool that aids in unveiling the statistical behavior of the algorithm in settings of interest viz. an acceptable value of k for a given sample size and distribution. Moreover, Monte Carlo approximations of such closed form expressions have been shown in Dhurandhar and Dobra (J Mach Learn Res 9, 2008; ACM Trans Knowl Discov Data 3, 2009) to be a superior alternative in terms of speed and accuracy when compared with computing the moments directly using Monte Carlo. This work employs the semi-analytical methodology that was proposed recently to better understand the non-asymptotic behavior of learning algorithms.
Starting Page 259
Ending Page 272
Page Count 14
File Format PDF
ISSN 18688071
Journal International Journal of Machine Learning and Cybernetics
Volume Number 4
Issue Number 4
e-ISSN 1868808X
Language English
Publisher Springer Berlin Heidelberg
Publisher Date 2012-04-27
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword kNN Moments Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type Text
Resource Type Article
Subject Artificial Intelligence Computer Vision and Pattern Recognition Software
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