Parameterized Learnability of k-Juntas and Related Problems
- Cite this paper as:
- Arvind V., Köbler J., Lindner W. (2007) Parameterized Learnability of k-Juntas and Related Problems. In: Hutter M., Servedio R.A., Takimoto E. (eds) Algorithmic Learning Theory. ALT 2007. Lecture Notes in Computer Science, vol 4754. Springer, Berlin, Heidelberg
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-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.
Subject ClassificationLearning theory computational complexity
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