A novel hybrid CNN–SVM classifier for recognizing handwritten digits

This paper presents a hybrid model of integrating the synergy of two superior classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM), which have proven results in recognizing different types of patterns. In this model, CNN works as a trainable feature extractor and SVM perf...

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Veröffentlicht in:	Pattern recognition Jg. 45; H. 4; S. 1318 - 1325
Hauptverfasser:	Niu, Xiao-Xiao, Suen, Ching Y.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.04.2012 Elsevier
Schlagworte:	Applied sciences Artificial intelligence Classifiers Computer science; control theory; systems Connectionism. Neural networks Convolutional Neural Network Digits Exact sciences and technology Handwritten digit recognition Hybrid model Information, signal and communications theory Mathematical models Pattern recognition Recognition Rejection Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Support Vector Machine Support vector machines Telecommunications and information theory Convolutional Neural Network Hybrid model Support Vector Machine Handwritten digit recognition Performance evaluation Automatic classification Cellular neural nets Support vector machine Pattern recognition Neural network Signal classification Database Handwritten character recognition Manuscript character
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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