Add to ORKGA proof that a concept class is learnable provided the Vapnik—Chervonenkis dimension is finite is given. The proof is more explicit than previous proofs and introduces two new parameters which allow bounds on the sample size obtained to be improved by a factor of approximately 4 log2(e)
Statistical learning theory chiefly studies restricted hypothesis classes, particularly those with f...
AbstractIn the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to esti...
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...
A proof that a concept is learnable provided the Vapnik-Chervonenkis dimension is finite is given. T...
AbstractWe consider the problem of learning a concept from examples in the distribution-free model b...
The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine lear...
We consider the problem of learning a concept from examples in the distribution-free model by Valian...
Abstract. The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis...
Abstract. Within the framework of pac-learning, we explore the learnability of concepts from samples...
AbstractWe prove a lower bound of Ω((1/ɛ)ln(1/δ)+VCdim(C)/ɛ) on the number of random examples requir...
Within the framework of pac-learning, we explore the learnability of concepts from samples using the...
. Within the framework of pac-learning, we explore the learnability of concepts from samples using t...
AbstractValiant's protocol for learning is extended to the case where the distribution of the exampl...
Lecture Notes in Artificial Intelligence 744, 279-287, 1993The Vapnik-Chervonenkis (VC) dimension is...
Statistical learning theory chiefly studies restricted hypothesis classes, particularly those with f...
AbstractIn the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to esti...
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...
A proof that a concept is learnable provided the Vapnik-Chervonenkis dimension is finite is given. T...
AbstractWe consider the problem of learning a concept from examples in the distribution-free model b...
The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine lear...
We consider the problem of learning a concept from examples in the distribution-free model by Valian...
Abstract. The Vapnik-Chervonenkis (V-C) dimension is an important combinatorial tool in the analysis...
Abstract. Within the framework of pac-learning, we explore the learnability of concepts from samples...
AbstractWe prove a lower bound of Ω((1/ɛ)ln(1/δ)+VCdim(C)/ɛ) on the number of random examples requir...
Within the framework of pac-learning, we explore the learnability of concepts from samples using the...
. Within the framework of pac-learning, we explore the learnability of concepts from samples using t...
AbstractValiant's protocol for learning is extended to the case where the distribution of the exampl...
Lecture Notes in Artificial Intelligence 744, 279-287, 1993The Vapnik-Chervonenkis (VC) dimension is...
Statistical learning theory chiefly studies restricted hypothesis classes, particularly those with f...
AbstractIn the PAC-learning model, the Vapnik-Chervonenkis (VC) dimension plays the key role to esti...
textabstractA stochastic model of learning from examples has been introduced by Valiant [1984]. This...