Machine Learning and Natural Language Processing Lab

Diskriminatives... |--| Logische Markov... |--| SeqLog und Theo... |--| Logical Decisio... |--| Implementierung... |--| Learning from G... |--| Learning Patter... |--| SCGEM - A Fast ... |--| Informationsext... |--| Tranformation-B... |--| EM and Gradient... |--| KDD Cup 2002 - ... |--| Analysis of Res... |--| Objektorientier... |--|

Student's Project

Tranformation-Based Regression

Björn Bringmann, 2002

In work and the paper, Transformation-Based Regression (TBR), a novel rule-based, symbolic regression technique based on Transformation-Based Learning (TBL) is introduced. 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 particulary 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. The algorithm and its implementation are explained with a focus to the optimizations in the rule-searching part. In the paper the capabilities and limitations of the approach in two highly complex real-world domains, musicology and speech synthesis are demonstrated.