2 research outputs found
Learning to Predict Combinatorial Structures
The major challenge in designing a discriminative learning algorithm for
predicting structured data is to address the computational issues arising from
the exponential size of the output space. Existing algorithms make different
assumptions to ensure efficient, polynomial time estimation of model
parameters. For several combinatorial structures, including cycles, partially
ordered sets, permutations and other graph classes, these assumptions do not
hold. In this thesis, we address the problem of designing learning algorithms
for predicting combinatorial structures by introducing two new assumptions: (i)
The first assumption is that a particular counting problem can be solved
efficiently. The consequence is a generalisation of the classical ridge
regression for structured prediction. (ii) The second assumption is that a
particular sampling problem can be solved efficiently. The consequence is a new
technique for designing and analysing probabilistic structured prediction
models. These results can be applied to solve several complex learning problems
including but not limited to multi-label classification, multi-category
hierarchical classification, and label ranking.Comment: PhD thesis, Department of Computer Science, University of Bonn
(submitted, December 2009