Add to ORKGWe consider a model of learning Boolean functions from examples generated by a uniform random walk on f0; 1gn. We give a polynomial time algorithm for learning decision trees and DNF formulas in this model. This is the rst ecient algorithm for learning these classes in a natural passive learning model where the learner has no in uence over the choice of examples used for learning. Supported by a Miller Postdoctoral Fellowship. ySupported by NSF grant 99-12342
We give an algorithm that learns any monotone Boolean function f: f1; 1gn! f1; 1g to any constant ac...
AbstractWe define the rank of a decision tree and show that for any fixed r, the class of all decisi...
AbstractIn this paper we consider an approach to passive learning. In contrast to the classical PAC ...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
AbstractWe consider a model of learning Boolean functions from examples generated by a uniform rando...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
We consider a natural framework of learning from correlated data, in which successive examples used ...
AbstractIn this paper we consider an approach to passive learning. In contrast to the classical PAC ...
AbstractWe define the rank of a decision tree and show that for any fixed r, the class of all decisi...
AbstractWe give an algorithm that with high probability properly learns random monotone DNF with t(n...
Abstract In a very strong positive result for passive learning algorithms, Bshouty et al. showed tha...
Abstract. We investigate the problem of learning Boolean functions with a short DNF representation u...
We give an algorithm that learns any monotone Boolean function f: f1; 1gn! f1; 1g to any constant ac...
AbstractWe define the rank of a decision tree and show that for any fixed r, the class of all decisi...
AbstractIn this paper we consider an approach to passive learning. In contrast to the classical PAC ...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
AbstractWe consider a model of learning Boolean functions from examples generated by a uniform rando...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
We consider a model of learning Boolean functions from examples generated by a uniform random walk o...
We consider a natural framework of learning from correlated data, in which successive examples used ...
AbstractIn this paper we consider an approach to passive learning. In contrast to the classical PAC ...
AbstractWe define the rank of a decision tree and show that for any fixed r, the class of all decisi...
AbstractWe give an algorithm that with high probability properly learns random monotone DNF with t(n...
Abstract In a very strong positive result for passive learning algorithms, Bshouty et al. showed tha...
Abstract. We investigate the problem of learning Boolean functions with a short DNF representation u...
We give an algorithm that learns any monotone Boolean function f: f1; 1gn! f1; 1g to any constant ac...
AbstractWe define the rank of a decision tree and show that for any fixed r, the class of all decisi...
AbstractIn this paper we consider an approach to passive learning. In contrast to the classical PAC ...