We address the problem of efficiently learning Naive Bayes classifiers under classconditional classification noise (CCCN). Naive Bayes classifiers rely on the hypothesis that the ...
In this paper, we present a general machine learning approach to the problem of deciding when to share probabilistic beliefs between agents for distributed monitoring. Our approac...
This paper considers a method for learning a distance metric in a fingerprinting system which identifies a query content by measuring the distance between the fingerprint of th...
— The main objetive of this paper is to improve the current status of learning object search. First, the current situation is analyzed and a theretical solution, based on relevan...
Abstract: We study the average-case learnability of DNF formulas in the model of learning from uniformly distributed random examples. We define a natural model of random monotone ...