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Symbol recognition with a new autonomously evolving classifier autoclass

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Symbol recognition with a new autonomously evolving classifier autoclass. / Angelov, Plamen; Kangin, Dmitry; Xiaowei, Zhou; Kolev, Denis.

2014 IEEE Conference on Evolving and Adaptive Intelligent Systems. 9781479933471 : IEEE, 2014. p. 1-6.

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

Harvard

Angelov, P, Kangin, D, Xiaowei, Z & Kolev, D 2014, Symbol recognition with a new autonomously evolving classifier autoclass. in 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems. IEEE, 9781479933471, pp. 1-6. https://doi.org/10.1109/EAIS.2014.6867482

APA

Angelov, P., Kangin, D., Xiaowei, Z., & Kolev, D. (2014). Symbol recognition with a new autonomously evolving classifier autoclass. In 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (pp. 1-6). IEEE. https://doi.org/10.1109/EAIS.2014.6867482

Vancouver

Angelov P, Kangin D, Xiaowei Z, Kolev D. Symbol recognition with a new autonomously evolving classifier autoclass. In 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems. 9781479933471: IEEE. 2014. p. 1-6 https://doi.org/10.1109/EAIS.2014.6867482

Author

Angelov, Plamen ; Kangin, Dmitry ; Xiaowei, Zhou ; Kolev, Denis. / Symbol recognition with a new autonomously evolving classifier autoclass. 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems. 9781479933471 : IEEE, 2014. pp. 1-6

Bibtex

@inproceedings{ca7d89b26abf472fb5758c8be31816ae,
title = "Symbol recognition with a new autonomously evolving classifier autoclass",
abstract = "A new algorithm for symbol recognition is proposed in this paper. It is based on the AutoClass classifier [1], [2], which itself is a version of the evolving fuzzy rule-based classifier eClass [3] in which AnYa[1] type of fuzzy rules and data density are used. In this classifier, symbol recognition task is divided into two stages: feature extraction, and recognition based on feature vector. This approach gives flexibility, allowing us to use various feature sets for one classifier. The feature extraction is performed by means of gist image descriptors[4] augmented by several additional features. In this method, we map the symbol images into the feature space, and then we apply AutoClass classifier in order to recognise them. Unlike many of the state-of-the-art algorithms, the proposed algorithm is evolving, i.e. it has a capability of incremental learning as well as ability to change its structure during the training phase. The classifier update is performed sample by sample, and we should not memorize the training set to provide recognition or further update. It gives a possibility to adapt the classifier to the broadening and changing data sets, which is especially useful for large scale systems improvement during exploitation. More, the classifier is computationally cheap, and it has shown stable recognition time during the increase of training data set size that is extremely important for online applications.",
keywords = "classifiers, evolving, image processing",
author = "Plamen Angelov and Dmitry Kangin and Zhou Xiaowei and Denis Kolev",
year = "2014",
month = jun,
doi = "10.1109/EAIS.2014.6867482",
language = "English",
pages = "1--6",
booktitle = "2014 IEEE Conference on Evolving and Adaptive Intelligent Systems",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Symbol recognition with a new autonomously evolving classifier autoclass

AU - Angelov, Plamen

AU - Kangin, Dmitry

AU - Xiaowei, Zhou

AU - Kolev, Denis

PY - 2014/6

Y1 - 2014/6

N2 - A new algorithm for symbol recognition is proposed in this paper. It is based on the AutoClass classifier [1], [2], which itself is a version of the evolving fuzzy rule-based classifier eClass [3] in which AnYa[1] type of fuzzy rules and data density are used. In this classifier, symbol recognition task is divided into two stages: feature extraction, and recognition based on feature vector. This approach gives flexibility, allowing us to use various feature sets for one classifier. The feature extraction is performed by means of gist image descriptors[4] augmented by several additional features. In this method, we map the symbol images into the feature space, and then we apply AutoClass classifier in order to recognise them. Unlike many of the state-of-the-art algorithms, the proposed algorithm is evolving, i.e. it has a capability of incremental learning as well as ability to change its structure during the training phase. The classifier update is performed sample by sample, and we should not memorize the training set to provide recognition or further update. It gives a possibility to adapt the classifier to the broadening and changing data sets, which is especially useful for large scale systems improvement during exploitation. More, the classifier is computationally cheap, and it has shown stable recognition time during the increase of training data set size that is extremely important for online applications.

AB - A new algorithm for symbol recognition is proposed in this paper. It is based on the AutoClass classifier [1], [2], which itself is a version of the evolving fuzzy rule-based classifier eClass [3] in which AnYa[1] type of fuzzy rules and data density are used. In this classifier, symbol recognition task is divided into two stages: feature extraction, and recognition based on feature vector. This approach gives flexibility, allowing us to use various feature sets for one classifier. The feature extraction is performed by means of gist image descriptors[4] augmented by several additional features. In this method, we map the symbol images into the feature space, and then we apply AutoClass classifier in order to recognise them. Unlike many of the state-of-the-art algorithms, the proposed algorithm is evolving, i.e. it has a capability of incremental learning as well as ability to change its structure during the training phase. The classifier update is performed sample by sample, and we should not memorize the training set to provide recognition or further update. It gives a possibility to adapt the classifier to the broadening and changing data sets, which is especially useful for large scale systems improvement during exploitation. More, the classifier is computationally cheap, and it has shown stable recognition time during the increase of training data set size that is extremely important for online applications.

KW - classifiers

KW - evolving

KW - image processing

U2 - 10.1109/EAIS.2014.6867482

DO - 10.1109/EAIS.2014.6867482

M3 - Conference contribution/Paper

SP - 1

EP - 6

BT - 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems

PB - IEEE

CY - 9781479933471

ER -