DocumentCode
409676
Title
Active machine learning using adaptive set estimation
Author
Joachim, D. ; Deller, J.R., Jr.
Author_Institution
Dept. of Electr. Engr. & Comput. Sci., Tulane Univ., New Orleans, LA, USA
Volume
1
fYear
2003
fDate
9-12 Nov. 2003
Firstpage
596
Abstract
The class of set-theoretic identification and filtering methods known as the optimal bounding ellipsoid (OBE) algorithms offer significant advantages in active machine learning tasks. Benefits include adaptive and intelligent classification over large data sets, elimination of redundancy and outliers, and automated assessment of innovation in observations. This paper presents formal links between the OBE algorithms and active machine learning solutions.
Keywords
adaptive estimation; filtering theory; learning (artificial intelligence); set theory; active machine learning; adaptive classification; adaptive set estimation; automated innovation assessment; filtering method; intelligent classification; optimal bounding ellipsoid algorithm; outlier elimination; redundancy elimination; set-theoretic identification; Aggregates; Covariance matrix; Ellipsoids; Learning systems; Machine learning; Machine learning algorithms; Particle measurements; Recursive estimation; State estimation; Strips;
fLanguage
English
Publisher
ieee
Conference_Titel
Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on
Print_ISBN
0-7803-8104-1
Type
conf
DOI
10.1109/ACSSC.2003.1291980
Filename
1291980
Link To Document