Most classification algorithms are “passive”, in that they assign a class label to each instance
based only on the description given, even if that description is incomplete. By contrast, an …

We give an overview of the fastest known algorithms for learning various expressive classes
of Boolean functions in the Probably Approximately Correct (PAC) learning model. In …

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 …

Current knowledge representation research has sought to provide schemes for encoding
knowledge about how a given system behaves, with the goal being accuracy and utility …

We investigate the problem of learning disjunctions of counting functions, generalizations of
parity and modulo functions, with equivalence and membership queries. We prove that, for …

Some recent work in computational learning theory has focused on the complexity of
teaching by examples. In this paper we study two combinatorial measures expressing the …

In order to systematize existing results, we propose to analyze the learnability of boolean
functions computed by an algebraically defined model, programs over monoids. The …

We investigate the learnability of the class κ-ZOTF n of disjunctions of (at most) κk zero-one
threshold functions with queries. We describe a poly (n)-time algorithm that identifies any …

Although many learning problems can be reduced to learning Boolean functions, in many
cases a more efficient learning algorithm can be derived when the problem is considered …

Multiplicative weight-update algorithms such as Winnow and Weighted Majority have been
studied extensively due to their on-line mistake bounds' logarithmic dependence on N, the …