Chunking with support vector machines Export

@misc{citeulike:1121693, abstract = {We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy...}, author = {Kudo, T. and Matsumoto, Y.}, citeulike-article-id = {1121693}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9541}, keywords = {bio, chunking, representation, svm}, posted-at = {2007-02-25 23:44:59}, priority = {0}, title = {Chunking with support vector machines}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9541}, year = {2001} }

TY - GEN ID - citeulike:1121693 L3 - citeulike-article-id:1121693 N2 - We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy... TI - Chunking with support vector machines KW - bio KW - chunking KW - representation KW - svm AU - Kudo, T AU - Matsumoto, Y PY - 2001/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9541 ER -