DocumentCode
2040642
Title
Information theoretic upper bounds on the number of distinguishable classes
Author
Keller, Catherine M. ; Ho, Mantak ; Basu, Prithwish ; Whipple, Gary H.
Author_Institution
MIT Lincoln Lab., Lexington, MA, USA
fYear
2013
fDate
3-6 Nov. 2013
Firstpage
1279
Lastpage
1285
Abstract
This paper examines data driven information theoretic upper bounds on the number of classes that can be distinguished by machine-learning classification systems as a function of the signal-to-noise ratio (SNR) of the features. Fano upper bounds are derived with desired classification error as a parameter. A simulation example is used to explore the bounds.
Keywords
learning (artificial intelligence); signal classification; Fano upper bounds; SNR; classification error; distinguishable classes; information theoretic upper bounds; machine-learning classification systems; signal-to-noise ratio; Covariance matrices; RLC circuits; Random variables; Signal to noise ratio; Training; Training data; Upper bound;
fLanguage
English
Publisher
ieee
Conference_Titel
Signals, Systems and Computers, 2013 Asilomar Conference on
Conference_Location
Pacific Grove, CA
Print_ISBN
978-1-4799-2388-5
Type
conf
DOI
10.1109/ACSSC.2013.6810500
Filename
6810500
Link To Document