The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Support vector machines
Abstract
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for the problem of classification and later generalized to various other situations. They are based on principles of statistical learning theory and convex optimization, and are currently used in various domains of application, including bioinformatics, text categorization, and computer vision. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification
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Cites
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Sentiment Analysis and Opinion Mining
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Support vector machines using GMM supervectors for speaker verification
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Drug design by machine learning: support vector machines for pharmaceutical data analysis
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Chou, K.-C., & Cai, Y.-D. (2002) · Journal of Biological Chemistry · doi:10.1074/jbc.m204161200 · Cited by 483
Sun, W., Zheng, Y., Yang, K., Zhang, Q., Shah, A. A., Wu, Z., Sun, Y., Feng, L., Chen, D., Xiao, Z., et al. (2019) · Science Advances · doi:10.1126/sciadv.aay4275 · Cited by 344
Metadata
- Authors
- Mammone, A., Turchi, M., & Cristianini, N.
- Venue
- WIREs Computational Statistics
- Publisher
- Wiley
- Published
- 2009-11
- Volume
- 1
- Issue
- 3
- Pages
- 283-289
- ISSN
- 1939-0068 (ISSN-L)
- 1939-5108
- OpenAlex
- W2012908927
- Cited by
- 699
- References
- 18
- FWCI
- 28.19
- Type
- journal article
- Language
- en
- Open access
- closed
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