ECML PKDD 2008
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
15 - 19 September 2008, Antwerp, Belgium
Paper abstractExact and Approximate Inference for Annotating Graphs with Structural SVMsThoralf Klein - MPI for Computer Science, GermanyUlf Brefeld - TU Berlin, Germany Tobias Scheffer - MPI for Computer Science, Germany Session: Support Vector Machines Springer Link: http://dx.doi.org/10.1007/978-3-540-87479-9_58 Training processes of structured prediction models such as structural SVMs involve frequent computations of the maximum-a-posteriori (MAP) prediction given a parameterized model. For specific output structures such as sequences or trees, MAP estimates can be computed efficiently by dynamic programming algorithms such as the Viterbi algorithm and the CKY parser. However, when the output structures can be arbitrary graphs, exact calculation of the MAP estimate is an NP-complete problem. In this paper, we compare exact inference and approximate inference for labeling graphs. We study the exact junction tree and the approximate loopy belief propagation and sampling algorithms in terms of performance and ressource requirements. |