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Smart sampling and incremental function learning for very large high dimensional data

Loyola, Diego und Pedergnana, Mattia und Gimeno García, Sebastián (2016) Smart sampling and incremental function learning for very large high dimensional data. Neural Networks, 78, Seiten 75-87. Elsevier. DOI: 10.1016/j.neunet.2015.09.001. ISSN 0893-6080.

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Offizielle URL: http://dx.doi.org/10.1016/j.neunet.2015.09.001

Kurzfassung

Very large high dimensional data are common nowadays and they impose new challenges to data-driven and data-intensive algorithms. Computational Intelligence techniques have the potential to provide powerful tools for addressing these challenges, but the current literature focuses mainly on handling scalability issues related to data volume in terms of sample size for classification tasks. This work presents a systematic and comprehensive approach for optimally handling regression tasks with very large high dimensional data. The proposed approach is based on smart sampling techniques for minimizing the number of samples to be generated by using an iterative approach that creates new sample sets until the input and output space of the function to be approximated are optimally covered. Incremental function learning takes place in each sampling iteration, the new samples are used to fine tune the regression results of the function learning algorithm. The accuracy and confidence levels of the resulting approximation function are assessed using the probably approximately correct computation framework. The smart sampling and incremental function learning techniques can be easily used in practical applications and scale well in the case of extremely large data. The feasibility and good results of the proposed techniques are demonstrated using benchmark functions as well as functions from real-world problems.

Dokumentart:Zeitschriftenbeitrag
Titel:Smart sampling and incremental function learning for very large high dimensional data
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID
Loyola, DiegoDLR-IMFNICHT SPEZIFIZIERT
Pedergnana, MattiaDLR-IMFNICHT SPEZIFIZIERT
Gimeno García, SebastiánDLR-IMFNICHT SPEZIFIZIERT
Datum:2016
Erschienen in:Neural Networks
Referierte Publikation:Ja
In Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:78
DOI :10.1016/j.neunet.2015.09.001
Seitenbereich:Seiten 75-87
Herausgeber:
HerausgeberInstitution und/oder E-Mail-Adresse der Herausgeber
Doya, KenjiOkinawa Inst. of Science & Tech., Onna, Okinawa, Japan
Deliang, WangOhio State University, Columbus, Ohio, USA
Verlag:Elsevier
Name der Reihe:Special Issue on "Neural Network Learning in Big Data"
ISSN:0893-6080
Status:veröffentlicht
Stichwörter:High dimensional function approximation; Sampling discrepancy; Design of experiments; Probably approximately correct computation; Function learning; Neural networks
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben Ozon-SAF
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Atmosphärenprozessoren
Hinterlegt von: Loyola, Dr.-Ing. Diego
Hinterlegt am:25 Mai 2012 08:30
Letzte Änderung:09 Sep 2016 16:54

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