On Restricted-Focus-of-Attention Learnability of Boolean Functions

In the k-Restricted-Focus-of-Attention (k-RFA) model, only k of the n attributes of each example are revealed to the learner, although the set of visible attributes in each example is determined by the learner. While the k-RFA model is a natural extension of the PAC model, there are also significant differences. For example, it was previously known that learnability in this model is not characterized by the VC-dimension and that many PAC learning algorithms are not applicable in the k-RFA setting. In this paper we further explore the relationship between the PAC and k-RFA models, with several interesting results. First, we develop an information-theoretic characterization of k-RFA learnability upon which we build a general tool for proving hardness results. We then apply this and other new techniques for studying RFA learning to two particularly expressive function classes, k-decision-lists (k-DL) and k-TOP, the class of thresholds of parity functions in which each parity function ta...

• Type: Journal
• Year: 1998
• Where: ML
• Authors: Andreas Birkendorf, Eli Dichterman, Jeffrey C. Jackson, Norbert Klasner, Hans-Ulrich Simon
• Keywords: Algorithm, K-rfa Model, Machine Learning, ML 1998, Model