Using output codes to boost multiclass learning problems

@inproceedings{citeulike:1410155, abstract = {. This paper describes a new technique for solving multiclass learning problems by combining Freund and Schapire's boosting algorithm with the main ideas of Dietterich and Bakiri's method of error-correcting output codes (ECOC). Boosting is a general method of improving the accuracy of a given base or \"weak\" learning algorithm. ECOC is a robust method of solving multiclass learning problems by reducing to a sequence of two-class problems. We show that our new hybrid method has advantages of...}, author = {Schapire, Robert E. }, booktitle = {Proc. 14th International Conference on Machine Learning}, citeulike-article-id = {1410155}, keywords = {boosting}, pages = {313--321}, posted-at = {2008-05-14 12:36:44}, priority = {2}, publisher = {Morgan Kaufmann}, title = {Using output codes to boost multiclass learning problems}, url = {http://citeseer.ist.psu.edu/4853.html}, year = {1997} }

TY - CONF ID - citeulike:1410155 L3 - citeulike-article-id:1410155 TI - Using output codes to boost multiclass learning problems T2 - Proc. 14th International Conference on Machine Learning SP - 313 EP - 321 PB - Morgan Kaufmann N2 - . This paper describes a new technique for solving multiclass learning problems by combining Freund and Schapire's boosting algorithm with the main ideas of Dietterich and Bakiri's method of error-correcting output codes (ECOC). Boosting is a general method of improving the accuracy of a given base or "weak" learning algorithm. ECOC is a robust method of solving multiclass learning problems by reducing to a sequence of two-class problems. We show that our new hybrid method has advantages of... KW - boosting AU - Schapire, Robert PY - 1997/// UR - http://citeseer.ist.psu.edu/4853.html ER -