Simple Probabilistic Predictions for Support Vector Regression
Date Issued
2004
Date
2004
Author(s)
Weng, Ruby-C.
DOI
20060927122850679786
Abstract
Support vector regression (SVR) has been popular in the past decade, but it provides only an estimated target
value instead of predictive probability intervals. Many work have addressed this issue but sometimes the SVR formula
must be modified. This paper presents a rather simple and direct approach to construct such intervals. We assume that
the conditional distribution of the target value depends on its input only through the predicted value, and propose to
model this distribution by simple functions. Experiments show that the proposed approach gives predictive intervals
with competitive coverages with Bayesian SVR methods.
Publisher
臺北市:國立臺灣大學資訊工程學系
Type
other
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