Stream-suitable optimization algorithms for some soft-margin support vector machine variants
Soft-margin support vector machines (SVMs) are an important class of classification models that are well known to be highly accurate in a variety of settings and over many applications. The training of SVMs usually requires that the data be available all at once, in batch. The Stochastic majorizatio...
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| Vydané v: | Japanese journal of statistics and data science Ročník 1; číslo 1; s. 81 - 108 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Singapore
Springer Singapore
01.06.2018
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| Predmet: | |
| ISSN: | 2520-8756, 2520-8764 |
| On-line prístup: | Získať plný text |
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