Learning from noisy data and Markovian processes

Abstract

We discuss more realistic models of computational learning. We extend the existing literature on the Probably Approximately Correct (PAC) framework to finite Markov chains in two directions by considering: (1) the presence of classification noise (specifically assuming that the training data has currupted labelled examples), and (2) real valued function learning. In both cases we address the key issue of determining how many training examples must be presented to the learner in the learning phase for the learning to be successful under the PAC paradigm