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Recursive SVM based on TEDA

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Recursive SVM based on TEDA. / Kangin, Dmitry; Angelov, Plamen.

Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. ed. / Alexander Gammerman; Vladimir Vovk; Harris Papadopoulos. Cham : Springer, 2015. p. 156-168 (Lecture Notes in Computer Science; Vol. 9047).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Kangin, D & Angelov, P 2015, Recursive SVM based on TEDA. in A Gammerman, V Vovk & H Papadopoulos (eds), Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Lecture Notes in Computer Science, vol. 9047, Springer, Cham, pp. 156-168. https://doi.org/10.1007/978-3-319-17091-6_11

APA

Kangin, D., & Angelov, P. (2015). Recursive SVM based on TEDA. In A. Gammerman, V. Vovk, & H. Papadopoulos (Eds.), Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings (pp. 156-168). (Lecture Notes in Computer Science; Vol. 9047). Springer. https://doi.org/10.1007/978-3-319-17091-6_11

Vancouver

Kangin D, Angelov P. Recursive SVM based on TEDA. In Gammerman A, Vovk V, Papadopoulos H, editors, Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. Cham: Springer. 2015. p. 156-168. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-17091-6_11

Author

Kangin, Dmitry ; Angelov, Plamen. / Recursive SVM based on TEDA. Statistical learning and data sciences: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings. editor / Alexander Gammerman ; Vladimir Vovk ; Harris Papadopoulos. Cham : Springer, 2015. pp. 156-168 (Lecture Notes in Computer Science).

Bibtex

@inproceedings{82a5cb78864a4bc89bcbc881dbc7e80b,
title = "Recursive SVM based on TEDA",
abstract = "The new method for incremental learning of SVM model incorporating recently proposed TEDA approach is proposed. The method updates the widely renowned incremental SVM approach, as well as introduces new TEDA and RDE kernels which are learnable and capable of adaptation to data. The slack variables are also adaptive and depend on each point{\textquoteright}s {\textquoteleft}importance{\textquoteright} combining the outliers detection with SVM slack variables to deal with misclassifications. Some suggestions on the evolving systems based on SVM are also provided. The examples of image recognition are provided to give a {\textquoteleft}proof of concept{\textquoteright} for the method.",
keywords = "SVM, TEDA, Incremental learning, Evolving system",
author = "Dmitry Kangin and Plamen Angelov",
year = "2015",
doi = "10.1007/978-3-319-17091-6_11",
language = "English",
isbn = "9783319170909",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "156--168",
editor = "Alexander Gammerman and Vladimir Vovk and Harris Papadopoulos",
booktitle = "Statistical learning and data sciences",

}

RIS

TY - GEN

T1 - Recursive SVM based on TEDA

AU - Kangin, Dmitry

AU - Angelov, Plamen

PY - 2015

Y1 - 2015

N2 - The new method for incremental learning of SVM model incorporating recently proposed TEDA approach is proposed. The method updates the widely renowned incremental SVM approach, as well as introduces new TEDA and RDE kernels which are learnable and capable of adaptation to data. The slack variables are also adaptive and depend on each point’s ‘importance’ combining the outliers detection with SVM slack variables to deal with misclassifications. Some suggestions on the evolving systems based on SVM are also provided. The examples of image recognition are provided to give a ‘proof of concept’ for the method.

AB - The new method for incremental learning of SVM model incorporating recently proposed TEDA approach is proposed. The method updates the widely renowned incremental SVM approach, as well as introduces new TEDA and RDE kernels which are learnable and capable of adaptation to data. The slack variables are also adaptive and depend on each point’s ‘importance’ combining the outliers detection with SVM slack variables to deal with misclassifications. Some suggestions on the evolving systems based on SVM are also provided. The examples of image recognition are provided to give a ‘proof of concept’ for the method.

KW - SVM

KW - TEDA

KW - Incremental learning

KW - Evolving system

U2 - 10.1007/978-3-319-17091-6_11

DO - 10.1007/978-3-319-17091-6_11

M3 - Conference contribution/Paper

SN - 9783319170909

T3 - Lecture Notes in Computer Science

SP - 156

EP - 168

BT - Statistical learning and data sciences

A2 - Gammerman, Alexander

A2 - Vovk, Vladimir

A2 - Papadopoulos, Harris

PB - Springer

CY - Cham

ER -