Support vector machine learning for interdependent and structured output spaces Export

@inproceedings{svmstruct, abstract = {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.}, address = {New York, NY, USA}, author = {Tsochantaridis, Ioannis and Hofmann, Thomas and Joachims, Thorsten and Altun, Yasemin}, booktitle = {ICML '04: Proceedings of the twenty-first international conference on Machine learning}, citeulike-article-id = {707334}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1015341}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1015330.1015341%3C}, doi = {10.1145/1015330.1015341%3C}, isbn = {1-58113-828-5}, keywords = {struct, svm}, location = {Banff, Alberta, Canada}, pages = {104+}, posted-at = {2009-02-19 10:37:53}, priority = {2}, publisher = {ACM}, title = {Support vector machine learning for interdependent and structured output spaces}, url = {http://dx.doi.org/10.1145/1015330.1015341%3C}, year = {2004} }

TY - CONF ID - svmstruct L3 - citeulike-article-id:707334 N2 - 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. CY - Banff, Alberta, Canada SN - 1-58113-828-5 AD - New York, NY, USA TI - Support vector machine learning for interdependent and structured output spaces PB - ACM T2 - ICML '04: Proceedings of the twenty-first international conference on Machine learning SP - 104 KW - struct KW - svm AU - Tsochantaridis, Ioannis AU - Hofmann, Thomas AU - Joachims, Thorsten AU - Altun, Yasemin PY - 2004/// UR - http://dx.doi.org/10.1145/1015330.1015341%3C ER -