Support vector machine learning for interdependent and structured output spaces


Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs such as multiple dependent output variables and structured output spaces. We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs. The resulting optimization problem is solved efficiently by a cutting plane algorithm that exploits the sparseness and structural decomposition of the problem. We demonstrate the versatility and effectiveness of our method on problems ranging from supervised grammar learning and named-entity recognition, to taxonomic text classification and sequence alignment.

DOI: 10.1145/1015330.1015341

Extracted Key Phrases

6 Figures and Tables

Showing 1-2 of 2 references
Showing 1-10 of 685 extracted citations
Citations per Year

1,168 Citations

Semantic Scholar estimates that this publication has received between 1,026 and 1,331 citations based on the available data.

See our FAQ for additional information.