Strings can be mapped into Hilbert spaces using feature maps such as the Parikh map. Languages can then be defined as the preimage of hyperplanes in the feature space, rather than using grammars or automata. These are the planar languages. In this paper we show that using techniques from kernel-based learning, we can represent and efficiently learn, from positive data alone, various linguistica...

Circuit expressions were introduced to provide a natural link between Computational Learning and certain aspects of Structural Complexity. Upper and lower bounds on the learnability of circuit expressions are known. We study here the case in which the circuit expressions are of low (time-bounded) Kolmogorov complexity. We show that these are polynomial-time learnable from membership queries in ...

Motivated by recent work on quantum black-box query complexity, we consider quantum versions of two wellstudied models of learning Boolean functions: Angluin’s model of exact learning from membership queries and Valiant’s Probably Approximately Correct (PAC) model of learning from random examples. For each of these two learning models we establish a polynomial relationship between the number of...