The DOI matches its Crossref record; 103 of the 103 references it cites were found in a registry or an index.
Selecting training sets for support vector machines: a review
Open accessAbstract
Support vector machines (SVMs) are a supervised classifier successfully applied in a plethora of real-life applications. However, they suffer from the important shortcomings of their high time and memory training complexities, which depend on the training set size. This issue is especially challenging nowadays, since the amount of data generated every second becomes tremendously large in many domains. This review provides an extensive survey on existing methods for selecting SVM training data from large datasets. We divide the state-of-the-art techniques into several categories. They help understand the underlying ideas behind these algorithms, which may be useful in designing new methods to deal with this important problem. The review is complemented with the discussion on the future research pathways which can make SVMs easier to exploit in practice.
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“SVMs prove to be successful in nonlinear classification problems by mapping non-separable features into a higher dimensional space, a procedure known as the kernel trick which uses kernel functions such as Radial Basis Function (RBF) or polynomial.(62) In DT approaches, the training set is continuously split according…”
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Metadata
- Authors
- Nalepa, J., & Kawulok, M.
- Venue
- Artificial Intelligence Review
- Published
- 2018-01-03
- Volume
- 52
- Issue
- 2
- Pages
- 857-900
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2782485997
- Cited by
- 419
- References
- 103
- FWCI
- 17.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
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