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Rate-optimal Meta Learning of Classification Error

Abstract: 

Meta learning of optimal classifier error rates allows an experimenter to empirically estimate the intrinsic ability of any estimator to discriminate between two populations, circumventing the difficult problem of estimating the optimal Bayes classifier. To this end we propose a weighted nearest neighbor (WNN) graph estimator for a tight bound on the Bayes classification error; the Henze-Penrose (HP) divergence. Similar to recently proposed HP estimators [berisha2016], the proposed estimator is non-parametric and does not require density estimation. However, unlike previous approaches the proposed estimator is rate-optimal, i.e., its mean squared estimation error (MSEE) decays to zero at the fastest possible rate of O(1/M+1/N) where M,N are the sample sizes of the respective populations. We illustrate the proposed WNN meta estimator for several simulated and real data sets.

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Paper Details

Authors:
Morteza Noshad, Alfred Hero
Submitted On:
16 April 2018 - 12:28am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Alfred O. Hero
Paper Code:
MLSP-P2.7
Document Year:
2018
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icassp-poster-v3.pdf

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[1] Morteza Noshad, Alfred Hero, "Rate-optimal Meta Learning of Classification Error", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2906. Accessed: Dec. 11, 2018.
@article{2906-18,
url = {http://sigport.org/2906},
author = {Morteza Noshad; Alfred Hero },
publisher = {IEEE SigPort},
title = {Rate-optimal Meta Learning of Classification Error},
year = {2018} }
TY - EJOUR
T1 - Rate-optimal Meta Learning of Classification Error
AU - Morteza Noshad; Alfred Hero
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2906
ER -
Morteza Noshad, Alfred Hero. (2018). Rate-optimal Meta Learning of Classification Error. IEEE SigPort. http://sigport.org/2906
Morteza Noshad, Alfred Hero, 2018. Rate-optimal Meta Learning of Classification Error. Available at: http://sigport.org/2906.
Morteza Noshad, Alfred Hero. (2018). "Rate-optimal Meta Learning of Classification Error." Web.
1. Morteza Noshad, Alfred Hero. Rate-optimal Meta Learning of Classification Error [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2906