Transformation-Based Regression

Abstract

In this paper, we introduce Transformation-Based Regression (TBR), a novel rule-based, symbolic regression technique based on Transformation-Based Learning (TBL). Although Transformation-Based Learning has been introduced already a couple of years ago, it has not yet been considered for regression-type tasks. The proposed method should be particularly useful for learning from examples with a given neighborhood relation, where the dependent variable of one example also depends on neighboring examples. Thus, the method should have a potential for learning from sequence and spatial data. In the paper, we demonstrate the capabilities and limitations of the approach in two highly complex real-world domains, musicology and speech synthesis.