期刊
PATTERN RECOGNITION
卷 45, 期 4, 页码 1318-1325出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.09.021
关键词
Hybrid model; Convolutional Neural Network; Support Vector Machine; Handwritten digit recognition
资金
- NSERC, the Natural Sciences and Engineering Research Council of Canada
- Concordia University
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 performs as a recognizer. This hybrid model automatically extracts features from the raw images and generates the predictions. Experiments have been conducted on the well-known MNIST digit database. Comparisons with other studies on the same database indicate that this fusion has achieved better results: a recognition rate of 99.81% without rejection, and a recognition rate of 94.40% with 5.60% rejection. These performances have been analyzed with reference to those by human subjects. (C) 2011 Elsevier Ltd. All rights reserved.
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