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What is Overview of Support Vector Regression about?

This document provides an overview of support vector regression (SVR). SVR is a generalization of support vector machines (SVM) that can be used for regression problems rather than classification. SVR works by finding a tube that best approximates the function being estimated, while balancing model complexity and error. It uses an epsilon-insensitive loss function that ignores errors within a certain threshold. The key advantages of SVR are its ability to generalize well to new data due to its use of kernel

Author

Nastaran Moosavi

Language

EN