Home > Research > Publications & Outputs > Evolving clustering, classification and regress...

Research

Associated organisational units

Electronic data

  • 07280528

    Rights statement: ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 882 KB, PDF document

Links

Text available via DOI:

View graph of relations

Evolving clustering, classification and regression with TEDA

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

Evolving clustering, classification and regression with TEDA. / Kangin, Dmitry; Angelov, Plamen Parvanov.
Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. p. 1-8.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Kangin, D & Angelov, PP 2015, Evolving clustering, classification and regression with TEDA. in Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8. https://doi.org/10.1109/IJCNN.2015.7280528

APA

Kangin, D., & Angelov, P. P. (2015). Evolving clustering, classification and regression with TEDA. In Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. https://doi.org/10.1109/IJCNN.2015.7280528

Vancouver

Kangin D, Angelov PP. Evolving clustering, classification and regression with TEDA. In Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN). IEEE. 2015. p. 1-8 doi: 10.1109/IJCNN.2015.7280528

Author

Kangin, Dmitry ; Angelov, Plamen Parvanov. / Evolving clustering, classification and regression with TEDA. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. pp. 1-8

Bibtex

@inproceedings{7a9f4d9402c94c04be5845e89af145f1,
title = "Evolving clustering, classification and regression with TEDA",
abstract = "In this article the novel clustering and regression methods TEDACluster and TEDAPredict methods are described additionally to recently proposed evolving classifier TEDAClass. The algorithms for classification, clustering and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive processing of large amounts of data. The framework is capable of computationally cheap exact update of data per sample, and can be used for training `from scratch'. All three algorithms are evolving that is they are capable of changing its own structure during the update stage, which allows to follow the changes within the model pattern.",
author = "Dmitry Kangin and Angelov, {Plamen Parvanov}",
note = "{\textcopyright}2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2015",
month = jul,
day = "12",
doi = "10.1109/IJCNN.2015.7280528",
language = "English",
pages = "1--8",
booktitle = "Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Evolving clustering, classification and regression with TEDA

AU - Kangin, Dmitry

AU - Angelov, Plamen Parvanov

N1 - ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2015/7/12

Y1 - 2015/7/12

N2 - In this article the novel clustering and regression methods TEDACluster and TEDAPredict methods are described additionally to recently proposed evolving classifier TEDAClass. The algorithms for classification, clustering and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive processing of large amounts of data. The framework is capable of computationally cheap exact update of data per sample, and can be used for training `from scratch'. All three algorithms are evolving that is they are capable of changing its own structure during the update stage, which allows to follow the changes within the model pattern.

AB - In this article the novel clustering and regression methods TEDACluster and TEDAPredict methods are described additionally to recently proposed evolving classifier TEDAClass. The algorithms for classification, clustering and regression are based on the recently proposed AnYa type fuzzy rule based system. The novel methods use the recently proposed TEDA framework capable of recursive processing of large amounts of data. The framework is capable of computationally cheap exact update of data per sample, and can be used for training `from scratch'. All three algorithms are evolving that is they are capable of changing its own structure during the update stage, which allows to follow the changes within the model pattern.

U2 - 10.1109/IJCNN.2015.7280528

DO - 10.1109/IJCNN.2015.7280528

M3 - Conference contribution/Paper

SP - 1

EP - 8

BT - Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE

ER -