Sciweavers

Share
DIS
2010
Springer

Speeding Up and Boosting Diverse Density Learning

11 years 10 months ago
Speeding Up and Boosting Diverse Density Learning
Abstract. In multi-instance learning, each example is described by a bag of instances instead of a single feature vector. In this paper, we revisit the idea of performing multi-instance classification based on a point-and-scaling concept by searching for the point in instance space with the highest diverse density. This is a computationally expensive process, and we describe several heuristics designed to improve runtime. Our results show that simple variants of existing algorithms can be used to find diverse density maxima more efficiently. We also show how significant increases in accuracy can be obtained by applying a boosting algorithm with a modified version of the diverse density algorithm as the weak learner.
James R. Foulds, Eibe Frank
Added 24 Jan 2011
Updated 24 Jan 2011
Type Journal
Year 2010
Where DIS
Authors James R. Foulds, Eibe Frank
Comments (0)
books