A robust machine learning approach for signal separation and classification

Simone Filice, Danilo Croce, Roberto Basili

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper a data-driven approach for signal separation over the digital domain is discussed. The proposed approach solves the problem as a classification task and it is widely experimented over electromagnetic signals in open scenarios. Results show that high levels of accuracy are reachable through a relatively easy learning method over simulated data.

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis - 6th Iberian Conference, IbPRIA 2013, Proceedings
Pages749-757
Number of pages9
DOIs
Publication statusPublished - 3 Sep 2013
Event6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013 - Funchal, Madeira, Portugal
Duration: 5 Jun 20137 Jun 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7887 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013
CountryPortugal
CityFunchal, Madeira
Period5/6/137/6/13

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Keywords

  • Machine Learning
  • Pattern recognition
  • Signal Processing
  • Support Vector Machines

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Filice, S., Croce, D., & Basili, R. (2013). A robust machine learning approach for signal separation and classification. In Pattern Recognition and Image Analysis - 6th Iberian Conference, IbPRIA 2013, Proceedings (pp. 749-757). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7887 LNCS). https://doi.org/10.1007/978-3-642-38628-2_89