Probably Approximately Correct Learning

Probably Approximately Correct Learning

This paper surveys some recent theoretical results on the efficiency of machine learning algorithms. The main tool described is the notion of Probably Approximately Correct (PAC) 1 earning, introduced by Valiant. We define this learning model and then look at sorne of the results obtained in it. We then consider some criticisms of the PAC model and the extensions proposed to address these criti...

Refutably Probably Approximately Correct Learning

On PAC Learning Algorithms for Rich Boolean Function Classes

We survey the fastest known algorithms for learning various expressive classes of Boolean functions in the Probably Approximately Correct (PAC) learning model.

A Survey of Quantum Learning Theory

This paper surveys quantum learning theory: the theoretical aspects of machine learning using quantum computers. We describe the main results known for three models of learning: exact learning frommembership queries, and Probably Approximately Correct (PAC) and agnostic learning from classical or quantum examples.

Part Overview of the Probably Approximately Correct PAC Learning Framework

Here we survey some recent theoretical results on the e ciency of machine learning algorithms The main tool described is the notion of Probably Approximately Correct PAC learning intro duced by Valiant We de ne this learning model and then look at some of the results obtained in it We then consider some criticisms of the PAC model and the extensions proposed to address these criticisms Finally ...