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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| Vydáno v: | Pattern recognition Ročník 45; číslo 4; s. 1318 - 1325 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Kidlington
Elsevier Ltd
01.04.2012
Elsevier |
| Témata: | |
| ISSN: | 0031-3203, 1873-5142 |
| On-line přístup: | Získat plný text |
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| Abstract | 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.
► We explored a new hybrid of Convolutional Neural Network and Support Vector Machine. ► Experiments were conducted on the MNIST database. ► The hybrid model has achieved better recognition and reliability performances. ► The best recognition rate was 99.81% without rejection. ► A reliability rate of 100% with 5.60% rejection was obtained. |
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| AbstractList | 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. 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. ► We explored a new hybrid of Convolutional Neural Network and Support Vector Machine. ► Experiments were conducted on the MNIST database. ► The hybrid model has achieved better recognition and reliability performances. ► The best recognition rate was 99.81% without rejection. ► A reliability rate of 100% with 5.60% rejection was obtained. |
| Author | Suen, Ching Y. Niu, Xiao-Xiao |
| Author_xml | – sequence: 1 givenname: Xiao-Xiao surname: Niu fullname: Niu, Xiao-Xiao email: archernxx@hotmail.com – sequence: 2 givenname: Ching Y. surname: Suen fullname: Suen, Ching Y. email: suen@encs.concordia.ca |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25604047$$DView record in Pascal Francis |
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| CODEN | PTNRA8 |
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| ContentType | Journal Article |
| Copyright | 2011 Elsevier Ltd 2015 INIST-CNRS |
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| Discipline | Computer Science Applied Sciences |
| EISSN | 1873-5142 |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | A novel hybrid CNN–SVM classifier for recognizing handwritten digits |
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