MultiResolution Complexity Analysis. A Novel Method for Partitioning Datasets into Regions of Different Classification Complexity

ARMANO, GIULIANO;TAMPONI, EMANUELE
2015-01-01

Abstract

Systems for complexity estimation typically aim to quantify the overall complexity of a domain, with the goal of comparing the hardness of different datasets or to associate a classification task to an algorithm that is deemed best suited for it. In this work we describe MultiResolution Complexity Analysis, a novel method for partitioning a dataset into regions of different classification complexity, with the aim of highlighting sources of complexity or noise inside the dataset. Initial experiments have been carried out on relevant datasets, proving the effectiveness of the proposed method.
2015
Inglese
ICPRAM 2015: Proceedings of the International Conference on Pattern Recognition Applications and Methods
978-989758076-5
1
334
341
8
2-s2.0-84938862107
4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015
Contributo
Esperti anonimi
January, 10-12, 2015
Lisbon, Portugal
internazionale
scientifica
no
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Armano, Giuliano; Tamponi, Emanuele
273
2
4.1 Contributo in Atti di convegno
reserved
info:eu-repo/semantics/conferencePaper
Files in This Item:
File Size Format  
2015-ICPRAM-armano.pdf

Solo gestori archivio

Type: versione editoriale
Size 186.85 kB
Format Adobe PDF
& nbsp; View / Open   Request a copy
186.85 kB Adobe PDF & nbsp; View / Open   Request a copy

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Questionnaire and social

Share on:
Impostazioni cookie