Feature Transformation Strategies for a Robot Learning Problem

@inproceedings{Lopes1998FeatureTS,
  title={Feature Transformation Strategies for a Robot Learning Problem},
  author={Lu{\'i}s Seabra Lopes and Luis M. Camarinha-Matos},
  year={1998}
}
  • Luís Seabra Lopes, Luis M. Camarinha-Matos
  • Published 1998
  • Computer Science
  • This chapter illustrates, with a case study from the robotized assembly domain, the importance of feature transformation. The specific problem that is addressed is learning failure diagnosis models for a pick-and-place operation. Several feature transformation strategies are evaluated on flat as well as hierarchical learning problems. The SKIL learning algorithm, previously proposed by the authors, is used in most experiments. A comparison with an oblique tree learning algorithm is also… CONTINUE READING

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