Publications Freek Stulp

@InProceedings{stulp06feature,
  title                    = {Feature Space Generation using Equation Discovery},
  author                   = {Freek Stulp and Mark Pfl\"uger and Michael Beetz},
  booktitle                = {Proceedings of the 29th German Conference on Artificial Intelligence (KI)},
  year                     = {2006},
  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.},
  bib2html_pubtype         = {Refereed Conference Paper},
  bib2html_rescat          = {Optimizing the Execution of Symbolic Robot Plans}
}