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Freek Stulp, Mark Pflüger, and Michael Beetz. Feature Space Generation using Equation Discovery. In Proceedings of the 29th German Conference on Artificial Intelligence (KI), 2006.
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Abstract
In Machine Learning, the success and performance of learning often critically depends on the feature space provided. This is particularly true for robot learning, where feature spaces are typically high dimensional and features are continuous. For many applications, it is common to design feature spaces manually. State variables are composed into higher level features using domain-specific knowledge. Unfortunately, manually designing these feature languages is tedious, because each new learning problem usually needs its own customized feature space. It is also error-prone, as relevant information in the original state space might be lost in the transformation. To overcome these problems, we propose an algorithm that automatically generates compact feature spaces, based on Equation Discovery. Our novel approach combines the strengths of Equation Discovery, being the compactness and interpretability of the resulting function, and Machine Learning, being its ability to approximate complex data.
BibTeX Entry
@InProceedings{stulp06feature,
author = {Freek Stulp and Mark Pfl\"uger and Michael Beetz},
title = {Feature Space Generation using Equation Discovery},
booktitle = {Proceedings of the 29th German Conference on Artificial Intelligence (KI)},
year = {2006},
bib2html_pubtype = {Refereed Conference Paper},
bib2html_rescat = {Robotics - Motor Control},
abstract = {In Machine Learning, the success and performance of learning often
critically depends on the feature space provided. This is particularly true
for robot learning, where feature spaces are typically high dimensional and
features are continuous. For many applications, it is common to design feature
spaces manually. State variables are composed into higher level features using
domain-specific knowledge. Unfortunately, manually designing these feature
languages is tedious, because each new learning problem usually needs its own
customized feature space. It is also error-prone, as relevant information in
the original state space might be lost in the transformation.
To overcome these problems, we propose an algorithm that automatically
generates compact feature spaces, based on Equation Discovery. Our novel
approach combines the strengths of Equation Discovery, being the compactness
and interpretability of the resulting function, and Machine Learning, being
its ability to approximate complex data.}
}
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