The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning

@article{Turney1996TheIO,
  title={The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning},
  author={Peter D. Turney},
  journal={CoRR},
  year={1996},
  volume={cs.LG/0212038}
}
A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi-dimensional feature space (also known as attribute-value descriptions). A teacher partitions a set of training examples into a finite number of classes. The task of the learning algorithm is to induce a concept from the training examples. In this paper, we formally distinguish three types of features: primary, contextual, and irrelevant… CONTINUE READING
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