DocumentCode
2055386
Title
Recursive classifiers
Author
Tapia, Elizabeth ; Gonzalez, J.C. ; Garcia, Javier ; Villena, Julio
Author_Institution
Nat. Univ. of Rosario, Argentina
fYear
2002
fDate
2002
Firstpage
185
Abstract
A recursive approach for the design of non-binary classifiers is proposed. By means of recursive coding models and the machine learning error-correcting output codes (ECOC) framework, learning in nonbinary output domains is reduced to a set of binary learning problems and a combination algorithm. This recursive learning formulation, hereafter named as RECOC learning, allows the design of general error adaptive classifiers, thus generalizing the binary-boosting concept.
Keywords
error correction codes; learning (artificial intelligence); pattern classification; binary learning problems; binary-boosting concept; combination algorithm; error adaptive classifiers; error-correcting output codes; machine learning ECOC framework; nonbinary classifiers; nonbinary output domains; recursive approach; recursive coding models; recursive error correcting codes; Additive noise; Algorithm design and analysis; Boosting; Equations; Error correction codes; Machine learning; Machine learning algorithms; Network address translation; Probability distribution; Turbo codes;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory, 2002. Proceedings. 2002 IEEE International Symposium on
Print_ISBN
0-7803-7501-7
Type
conf
DOI
10.1109/ISIT.2002.1023457
Filename
1023457
Link To Document