Quantitative evaluation of dependence among outputs in ECOC classifiers using mutual information based measures

Author

Masulli, Francesco ; Valentini, Giorgio

Author_Institution

Dipt. di Inf. e Scienze dell´´Inf, Genova Univ., Italy

Volume

2

fYear

2001

fDate

2001

Firstpage

784

Abstract

In previous work by the authors (2000), it has been experimentally shown that the implementation of error correcting output coding (ECOC) classification methods with an ensemble of parallel and independent nonlinear dichotomizers (ECOC PND) outperforms the implementation with a single monolithic multilayer perceptron (ECOC MLP). The low dependence of the errors on different codeword bits was qualitatively indicated as one of the main factors affecting this result. In this paper, they quantitatively evaluate the dependence of output errors in ECOC learning machines using mutual information based measures, and we study the relation between dependence of output errors and classification performances

Keywords

information theory; learning (artificial intelligence); learning systems; pattern classification; statistical analysis; classification performances; codeword bits; error correcting output coding classification methods; learning machines; mutual information based measures; nonlinear dichotomizers; output errors; quantitative evaluation; Electronic mail; Error correction; Error correction codes; Image coding; Image processing; Machine learning; Mutual information; Organizing; Parametric statistics; Performance evaluation;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.939459

Filename

939459