LPRANKBOOST AND COLUMN GENERATION
Kristiaan Pelckmans, Johan A. K. Suykens · 2008
We investigate the use of LPboost for combining a set of weak learning functions into a global ranking function for predicting the order of a new subject. The notion of risk is translated as an appropri- ate concordance score (related to AUC and Kendall's tau), while the regularization mechanism results in a sparse solution useful for discovering structure in the specific task at hand. The result can be analyzed as a global linear programming problem, while a column generation approach yields an time- and space- efficient implementation. 1