The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Comparative analysis of image classification algorithms based on traditional machine learning and deep learning
Abstract
Abstract Image classification is a hot research topic in today's society and an important direction in the field of image processing research. SVM is a very powerful classification model in machine learning. CNN is a type of feedforward neural network that includes convolution calculation and has a deep structure. It is one of the representative algorithms of deep learning. Taking SVM and CNN as examples, this paper compares and analyzes the traditional machine learning and deep learning image classification algorithms. This study found that when using a large sample mnist dataset, the accuracy of SVM is 0.88 and the accuracy of CNN is 0.98; when using a small sample COREL1000 dataset, the accuracy of SVM is 0.86 and the accuracy of CNN is 0.83. The experimental results in this paper show that traditional machine learning has a better solution effect on small sample data sets, and deep learning framework has higher recognition accuracy on large sample data sets.
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Cites
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Cited by
A Review on Deep-Learning-Based Cyberbullying Detection
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Metadata
- Authors
- Wang, P., Fan, E., & Wang, P.
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 141
- Pages
- 61-67
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W3046220160
- Cited by
- 847
- References
- 19
- FWCI
- 33.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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