Extracting support data for a given task

@misc{citeulike:3007875, abstract = {We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve a given classification task: using the Support Vector Algorithm to train three different types of handwritten digit classifiers, we observed that these types of classifiers construct their decision surface from strongly overlapping small ({\ss} 4\%) subsets of the data base. This finding opens up the possibility of compressing data bases significantly by disposing of the...}, author = {Sch, B. and Burges and Vapnik, V. }, citeulike-article-id = {3007875}, keywords = {svm}, posted-at = {2008-07-16 09:56:52}, priority = {2}, title = {Extracting support data for a given task}, url = {http://citeseer.ist.psu.edu/sch95extracting.html}, year = {1995} }

TY - GEN ID - citeulike:3007875 L3 - citeulike-article-id:3007875 TI - Extracting support data for a given task N2 - We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve a given classification task: using the Support Vector Algorithm to train three different types of handwritten digit classifiers, we observed that these types of classifiers construct their decision surface from strongly overlapping small (ß 4%) subsets of the data base. This finding opens up the possibility of compressing data bases significantly by disposing of the... KW - svm AU - Sch, B AU - Burges AU - Vapnik, V PY - 1995/// UR - http://citeseer.ist.psu.edu/sch95extracting.html ER -