Improved Classification Rates for Localized Algorithms under Margin Conditions
Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems...
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| Format: | eBook |
| Language: | English |
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Wiesbaden
Springer Fachmedien Wiesbaden
2020, 2020
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| Edition: | 1st ed. 2020 |
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| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Introduction to Statistical Learning Theory
- Histogram Rule: Oracle Inequality and Learning Rates
- Localized SVMs: Oracle Inequalities and Learning Rates