PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The cost of learning directed cuts
Thomas Gaertner and Gemma Garriga
In: ECML 2007, Oct 2007, Warsaw, Poland.

Abstract

Abstract. In this paper we investigate the problem of classifying ver- tices 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 learn- ability results with tight performance guarantees for active, online, as well as PAC learning. The advantage of this parameter over possible al- ternatives 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3753
Deposited By:Gemma Garriga
Deposited On:13 March 2009