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Multi-task learning for boosting with application to web search ranking
2010
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '10
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing the specifics of each learning task with task-specific parameters and the commonalities between them through shared parameters. This enables implicit data sharing and regularization. We evaluate our learning method on web-search ranking data sets from several countries. Here, multitask learning is particularly helpful
doi:10.1145/1835804.1835953
dblp:conf/kdd/ChapelleSVWZT10
fatcat:36ewsgphr5dohmy7k4xhk4mvuu