منابع مشابه
Parameterized Learnability of k -Juntas and Related Problems
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. Subject Cl...
متن کاملTesting Juntas
We show that a boolean valued function over n variables, where each variable ranges in an arbitrary probability space, can be tested for the property of depending on only J of them using a number of queries that depends only polynomially on J and the approximation parameter . We present several tests that require a number of queries that is polynomial in J and linear in −1. We show a non-adapti...
متن کاملLocal correction of juntas
A Boolean function f over n variables is said to be q-locally correctable if, given a black-box access to a function g which is ”close” to an isomorphism fσ of f , we can compute fσ(x) for any x ∈ Z 2 with good probability using q queries to g. We observe that any k-junta, that is, any function which depends only on k of its input variables, is O(2)-locally correctable. Moreover, we show that t...
متن کاملLearning Juntas in the Presence of Noise
We investigate the combination of two major challenges in computational learning: dealing with huge amounts of irrelevant information and learning from noisy data. It is shown that large classes of Boolean concepts that depend only on a small fraction of their variables—so-called juntas—can be learned efficiently from uniformly distributed examples that are corrupted by random attribute and cla...
متن کاملTesting Juntas (draft)
A function on n variables is called a k-junta if it depends on at most k of its variables. The problem of testing whether a function is a k-junta or is “far” from being a k-junta is a central problem in property testing and is closely related to the problem of learning high-dimensional data. In this note, we give an informal presentation of three recent algorithms for testing juntas efficiently.
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ژورنال
عنوان ژورنال: Theoretical Computer Science
سال: 2009
ISSN: 0304-3975
DOI: 10.1016/j.tcs.2009.07.003