In the context of computational learning, the concept of 'probably approximately correct' (PAC) learning provides a framework for understanding the efficiency and feasibility of learning algorithms.
No man, for any considerable period, can wear one face to himself and another to the multitude, without finally getting bewildered as to which may be the true.
