Algorithmic Theories of Learning

We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain eeectively learn concepts from a small number of examples, in spite of the fact that each example contains a huge amount of information? We provide a novel analysis for a model of robust concept learning (closely related to \margin classiiers"), and show that a relatively small number of examples are suucient to learn rich concept classes (including threshold functions, boolean formulae and polynomial surfaces). As a result, we obtain simple intuitive proofs for the generalization bounds of Support Vector Machines. In addition, the new algorithms have several advantages | they are faster, conceptually simpler, and highly resistant to noise. For example, a robust half-space can be PAC-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.