Using relevance to train a linear mixture of experts
Christopher C. Vogt, Garrison W. Cottrell, Richard K. Belew, Brian T. Bartell · 1996
A linear mixture of experts is used to combine three standard IR systems. The parameters for the mixture are determined automatically through training on document relevance assessments via optimization of a rank-order statistic which is empirically correlated with average precision. The mixture improves performance in some cases and degrades it in others, with the degradations possibly due to training techniques, model strength, and poor performance of the individual experts. 1 INTRODUCTION The mixture of experts approach is one which is gaining in popularity in many areas of computer science and artificial intelligence (e.g., [Jordan and Jacobs, 1994]) and one which is especially applicable to information retrieval, since in practice the sets of relevant documents returned by different IR algorithms (or experts) often have little overlap. In fact, the pooling method used by past TREC's to determine which documents are relevant can be viewed as a sort of mixture model on the grandest ...