Support Vector Regression Machines

作者：Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex Smola, Vladimir Vapnik · 年份：1996 · 被引用次数：4163 · 研究领域：Face and Expression Recognition、Neural Networks and Applications、Image Retrieval and Classification Techniques

A new regression technique based on concept of support vectors is introduced. We compare support vector regression with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimension&y of input space. This is a longer version of the paper appear in Advances in Neural Processing Systems 9 (proceedings of the 1996 conference)