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Soft Morphological Filtering Using a Fuzzy Model and its Application to Colour Image Processing

Generative binary codes

Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines

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Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines

Content Provider	Springer Nature Link
Author	Masulli, F. Valentini, G.
Copyright Year	2004
Abstract	Error Correcting Output Coding (ECOC) methods for multiclass classification present several open problems ranging from the trade-off between their error recovering capabilities and the learnability of the induced dichotomies to the selection of proper base learners and to the design of well-separated codes for a given multiclass problem. We experimentally analyse some of the main factors affecting the effectiveness of ECOC methods. We show that the architecture of ECOC learning machines influences the accuracy of the ECOC classifier, highlighting that ensembles of parallel and independent dichotomic Multi-Layer Perceptrons are well-suited to implement ECOC methods. We quantitatively evaluate the dependence among codeword bit errors using mutual information based measures, experimentally showing that a low dependence enhances the generalisation capabilities of ECOC. Moreover we show that the proper selection of the base learner and the decoding function of the reconstruction stage significantly affects the performance of the ECOC ensemble. The analysis of the relationships between the error recovering power, the accuracy of the base learners, and the dependence among codeword bits show that all these factors concur to the effectiveness of ECOC methods in a not straightforward way, very likely dependent on the distribution and complexity of the data.
Starting Page	285
Ending Page	300
Page Count	16
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	6
Issue Number	4
e-ISSN	1433755X
Language	English
Publisher	Springer-Verlag
Publisher Date	2004-01-01
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Coding Classification problems ECOC ensemble Ensemble of learning machines Error correcting output
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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Joint learning of error-correcting output codes and dichotomizers from data

On the Decoding Process in Ternary Error-Correcting Output Codes

Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines

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