Machine Learning - Special issue on context sensitivity and concept drift
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Semi-supervised learning using randomized mincuts
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Monotone Decision Trees in Polynomial Time
CCC '06 Proceedings of the 21st Annual IEEE Conference on Computational Complexity
On Path Cover Problems in Digraphs and Applications to Program Testing
IEEE Transactions on Software Engineering
Combinatorial Optimization: Theory and Algorithms
Combinatorial Optimization: Theory and Algorithms
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In this paper we investigate the problem of classifying vertices of a directed graph according to an unknown directed cut. We first consider the usual setting in which the directed cut is fixed. However, even in this setting learning is not possible without in the worst case needing the labels for the whole vertex set. By considering the size of the minimum path cover as a fixed parameter, we derive positive learnability results with tight performance guarantees for active, online, as well as PAC learning. The advantage of this parameter over possible alternatives is that it allows for an a priori estimation of the total cost of labelling all vertices. The main result of this paper is the analysis of learning directed cuts that depend on a hidden and changing context.