An algorithmic theory of learning: Robust concepts and random projection


We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain effectively learn concepts from a small number of examples despite the fact that each example contains a huge amount of information? We provide a novel algorithmic analysis via a model of robust concept learning (closely related to “margin classifiers”), and show that a relatively small number of examples are sufficient to learn rich concept classes. The new algorithms have several advantages—they are faster, conceptually simpler, and resistant to low levels of noise. For example, a robust half-space can be learned in linear time using only a constant number of training examples, regardless of the number of attributes. A general (algorithmic) consequence of the model, that “more robust concepts are easier to learn”, is supported by a multitude of psychological studies.


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Correspondence to Santosh Vempala.

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Editor: Shai Ben-David

A preliminary version of this paper appeared in the Proc. of the Symposium on the Foundations of Computer Science, 1999

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Arriaga, R.I., Vempala, S. An algorithmic theory of learning: Robust concepts and random projection. Mach Learn 63, 161–182 (2006).

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  • Learning
  • Cognition
  • Random projection
  • Robust concepts