Training weighted SVMs using a generalized Schlesinger-Kozinec algorithm

Autor:	Ben Goodrich, David W. Albrecht, Peter Tischer
Rok vydání:	2011
Předmět:	Support vector machine Nearest point Randomized weighted majority algorithm Training set Weighted Majority Algorithm business.industry Regular polygon Training (meteorology) Pattern recognition Artificial intelligence business Algorithm Mathematics
Zdroj:	IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.
Popis:	We approach the problem of applying nearest point algorithms to train weighted SVMs by introducing the concept of Weighted Reduced Convex Hulls (WRCHs). We describe some of the theoretical properties of WRCHs and show how their vertices may be found. The introduction of WRCHs provides an essential tool for understanding how weighted SVMs work and why they are important. Further, they allow us to generalize the Schlesinger-Kozinec (S-K) nearest point algorithm to operate over WRCHs. The result is a nearest point algorithm which is capable of training weighted SVMs without the need for inflating the training set size.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e1aef86cdc89a287c996ab544fe2535b https://doi.org/10.1109/iecon.2011.6119691 Zobrazit plný text záznamu