AbstractWe show that the class of monotone 2O(logn)-term DNF formulae can be PAC learned in polynomial time under the uniform distribution from random examples only. This is an exponential improvement over the best previous polynomial-time algorithms in this model, which could learn monotone o(log2n)-term DNF. We also show that various classes of small constant-depth circuits which compute monotone functions are PAC learnable in polynomial time under the uniform distribution. All of our results extend to learning under any constant-bounded product distribution
This note studies the learnability of the class k-term DNF with a bounded number of negations per ...
AbstractThe learnability of the class of exclusive-or expansions based on monotone DNF formulas is i...
We give an algorithm that learns any monotone Boolean function f: f1; 1gn! f1; 1g to any constant ac...
In 1984 Valiant introduced the distribution-independent model of Probably Approximately Correct (PAC...
AbstractWe show how to learn in polynomial time monotone d-term DNF formulae (formulae in disjunctiv...
We show how to learn in polynomial time monotone d-term DNF formulae (formulae in disjunctive normal...
AbstractWe give an algorithm that with high probability properly learns random monotone DNF with t(n...
AbstractIn this paper we extend the Monotone Theory to the PAC-learning Model with membership querie...
Since its introduction by Valiant in 1984, PAC learning of DNF expressions remains one of the centra...
Recently, Valiant introduced a computational model of learning, and gave a precice definition of lea...
Much work has been done on learning various classes of “simple ” monotone functions under the unifor...
We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions ...
. We consider the exact learnability of subclasses of Boolean formulas from membership queries alone...
We consider the problem of learning monotone Boolean functions over under the uniform distributi...
In this paper, we prove two general theorems on monotone Boolean functions which are useful for con...
This note studies the learnability of the class k-term DNF with a bounded number of negations per ...
AbstractThe learnability of the class of exclusive-or expansions based on monotone DNF formulas is i...
We give an algorithm that learns any monotone Boolean function f: f1; 1gn! f1; 1g to any constant ac...
In 1984 Valiant introduced the distribution-independent model of Probably Approximately Correct (PAC...
AbstractWe show how to learn in polynomial time monotone d-term DNF formulae (formulae in disjunctiv...
We show how to learn in polynomial time monotone d-term DNF formulae (formulae in disjunctive normal...
AbstractWe give an algorithm that with high probability properly learns random monotone DNF with t(n...
AbstractIn this paper we extend the Monotone Theory to the PAC-learning Model with membership querie...
Since its introduction by Valiant in 1984, PAC learning of DNF expressions remains one of the centra...
Recently, Valiant introduced a computational model of learning, and gave a precice definition of lea...
Much work has been done on learning various classes of “simple ” monotone functions under the unifor...
We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions ...
. We consider the exact learnability of subclasses of Boolean formulas from membership queries alone...
We consider the problem of learning monotone Boolean functions over under the uniform distributi...
In this paper, we prove two general theorems on monotone Boolean functions which are useful for con...
This note studies the learnability of the class k-term DNF with a bounded number of negations per ...
AbstractThe learnability of the class of exclusive-or expansions based on monotone DNF formulas is i...
We give an algorithm that learns any monotone Boolean function f: f1; 1gn! f1; 1g to any constant ac...