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Pattern Analysis and Applications

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01.02.2016 | Short Paper

Experimenting multiresolution analysis for identifying regions of different classification complexity

verfasst von: G. Armano, E. Tamponi

Erschienen in: Pattern Analysis and Applications | Ausgabe 1/2016

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Abstract

Systems for assessing the classification complexity of a dataset have received increasing attention in research activities on pattern recognition. These systems typically aim at quantifying the overall complexity of a domain, with the goal of comparing different datasets. In this work, we propose a method for partitioning a dataset into regions of different classification complexity, so to highlight sources of complexity inside the dataset. Experiments have been carried out on relevant datasets, proving the effectiveness of the proposed method.

Vorheriger Artikel A non-parametric symbolic approximate representation for long time series

Nächster Artikel Voiced/non-voiced speech classification using adaptive thresholding with bivariate EMD

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Of course, a similar definition can also be given for the dataset \(\mathbf {D}\).

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Titel: Experimenting multiresolution analysis for identifying regions of different classification complexity
verfasst von: G. Armano
E. Tamponi
Publikationsdatum: 01.02.2016
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 1/2016
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-014-0446-y

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