AbstractN. Linialet al.raised the question of how difficult the computation of the Vapnik–Červonenkis dimension of a concept class over a finite universe is. C. Papadimitriou and M. Yannakakis obtained a first answer using matrix representations of concept classes. However, this approach does not capture classes having exponential size, like monomials, which are encountered in learning theory. We choose a more natural representation, which leads us to redefine the VC DIMENSION problem. We establish that VC DIMENSION is∑p3-complete, thereby giving a rare natural example of a∑p3-complete problem
Vapnik Chervonenkis dimension is a basic combinatorial notion with applications in machine learnin...
We demonstrate that the Vapnik-Chervonenkis dimension of the class of monotone formulas over n varia...
textabstractA stochastic model of learning from examples has been introduced by Valiant [1984]. This...
AbstractA proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is fini...
Abstract. The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis...
Lecture Notes in Artificial Intelligence 744, 279-287, 1993The Vapnik-Chervonenkis (VC) dimension is...
We consider the problem of learning a concept from examples in the distribution-free model by Valian...
We show that the Vapnik-Chervonenkis dimension of Boolean monomials over n variables is at most n fo...
AbstractIn the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to esti...
AbstractWe consider the problem of learning a concept from examples in the distribution-free model b...
A proof that a concept is learnable provided the Vapnik-Chervonenkis dimension is finite is given. T...
AbstractWe begin with a brief tutorial on the problem of learning a finite concept class over a fini...
In the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to estimate the...
Proc. European Conference on Machine Learning, Lecture Notes in Artificial Intelligence 784, 415-418...
A proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is finite is gi...
Vapnik Chervonenkis dimension is a basic combinatorial notion with applications in machine learnin...
We demonstrate that the Vapnik-Chervonenkis dimension of the class of monotone formulas over n varia...
textabstractA stochastic model of learning from examples has been introduced by Valiant [1984]. This...
AbstractA proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is fini...
Abstract. The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis...
Lecture Notes in Artificial Intelligence 744, 279-287, 1993The Vapnik-Chervonenkis (VC) dimension is...
We consider the problem of learning a concept from examples in the distribution-free model by Valian...
We show that the Vapnik-Chervonenkis dimension of Boolean monomials over n variables is at most n fo...
AbstractIn the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to esti...
AbstractWe consider the problem of learning a concept from examples in the distribution-free model b...
A proof that a concept is learnable provided the Vapnik-Chervonenkis dimension is finite is given. T...
AbstractWe begin with a brief tutorial on the problem of learning a finite concept class over a fini...
In the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to estimate the...
Proc. European Conference on Machine Learning, Lecture Notes in Artificial Intelligence 784, 415-418...
A proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is finite is gi...
Vapnik Chervonenkis dimension is a basic combinatorial notion with applications in machine learnin...
We demonstrate that the Vapnik-Chervonenkis dimension of the class of monotone formulas over n varia...
textabstractA stochastic model of learning from examples has been introduced by Valiant [1984]. This...