Fast methods for kernelbased text analysis Export

@misc{citeulike:1121674, abstract = {Kernel-based learning (e.g., Support Vector Machines) has been successfully applied to many hard problems in Natural Language Processing (NLP). In NLP, although feature combinations are crucial to improving performance, they are heuristically selected. Kernel methods change this situation. The merit of the kernel methods is that effective feature combination is implicitly expanded without loss of generality and increasing the computational costs. Kernel-based text analysis shows an ...}, author = {Kudo, T. and Matsumoto, Y.}, citeulike-article-id = {1121674}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.678}, keywords = {chunking, svm}, posted-at = {2007-02-25 23:28:44}, priority = {0}, title = {Fast methods for kernelbased text analysis}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.678}, year = {2003} }

TY - GEN ID - citeulike:1121674 L3 - citeulike-article-id:1121674 N2 - Kernel-based learning (e.g., Support Vector Machines) has been successfully applied to many hard problems in Natural Language Processing (NLP). In NLP, although feature combinations are crucial to improving performance, they are heuristically selected. Kernel methods change this situation. The merit of the kernel methods is that effective feature combination is implicitly expanded without loss of generality and increasing the computational costs. Kernel-based text analysis shows an ... TI - Fast methods for kernelbased text analysis KW - chunking KW - svm AU - Kudo, T AU - Matsumoto, Y PY - 2003/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.678 ER -