You might also like

More about Improved Classification Rates for Localized Algorithms under Margin Conditions

• Book details
• Synopsis
• Trending among others

You get 154 loyalty points

Book synopsis

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, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance.

Book details

Book category Books in English Mathematics & science Mathematics Applied mathematics

• Full title: Improved Classification Rates for Localized Algorithms under Margin Conditions
• Author: Ingrid Karin Blaschzyk
• Language: English
• Binding: Paperback
• Number of pages: 126
• EAN: 9783658295905
• ID: 25825732
• Publisher: Springer Fachmedien Wiesbaden
• Weight: 454 g
• Dimensions: 210 × 148 × 9 mm
• Date of publishing: 19. March 2020

Trending among others

---

---

---