Learning k-DNF with noise in the attributes

This paper addresses the problem of learning boolean functions in query and mistake-bound models in the presence of irrelevant attributes. In learning a concept, a learner may observe a great many more attributes than those that the concept depends upon,...

Relative attributes learning aims to learn ranking functions describing the relative strength of attributes. Most of current learning approaches learn ranking functions for each attribute independently without considering possible intrinsic relatedness ...

We consider the problem of learning in the presence of irrelevant attributes in Valiant's PAC model (1984). In the PAC model, the goal of the learner is to produce an approximately correct hypothesis from random sample data. If the number of relevant ...