Parameterized learnability of juntas
We study the parameterized complexity of learning k-juntas and some variations of juntas. We show the hardness of learning k-juntas and subclasses of k-juntas in the PAC model by reductions from a W[2]-complete problem. On the other hand, as a consequence of a more general result, we show that k-juntas are exactly learnable with improper equivalence queries and access to a W[P] oracle. (C) 2009 Elsevier B.V. All rights reserved.
learning theory computational complexity