Standard
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/ISSN › Conference contribution/Paper › peer-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). doi: 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 -