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Abstract: The evaluation of dependence among output errors of multi-input multi-output learning machines can help us in designing well-behaved systems, highlighting hidden interactions among their internal components that can add noise to the learning process. By estimating the relations between performances and dependence among output errors, we can compare different models of learning machines in order to select the ones best suited to a particular problem. We distinguish between dependence among... (Update)
Context of citations to this paper: More
...this dependence and performances of ECOC methods. In this paper we use mutual information based measures of dependence among output errors [24] to quantitatively evaluate the dependence among errors of computed codeword bits. In this way we can study the relations between...
.... we use some mutual information based measures for the evaluation of dependence among outputs errors in a learning machine proposed in [10]. The main idea behind the application of those measures of dependence consists in interpreting the dependence among the outputs as the...
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BibTeX entry: (Update)
F. Masulli and G. Valentini. Mutual information methods for evaluating dependence among outputs in learning machines. Technical Report TR-01-02, DISI - Dipartimento di Informatica e Scienze dell' Informazione - Universita di Genova, 2001. ftp://ftp.disi.unige.it/person/ValentiniG/TR-01-02.ps.gz. http://citeseer.ist.psu.edu/masulli01mutual.html More
@techreport{ masulli01mutual,
author = "F. Masulli and G. Valentini",
title = "Mutual information methods for evaluating dependence among outputs in learning
machines",
number = "TR-01-02",
institution = "{DISI} --- Dipartimento di Informatica e Scienze dell' Informazione - Universit a
di Genova",
note = "ftp://ftp.disi.unige.it/person/ValentiniG/TR-01-02.ps.gz",
year = "2001",
url = "citeseer.ist.psu.edu/masulli01mutual.html" }
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