  Predictor combination aims to improve a (target) predictor of a learning task
based on the (reference) predictors of potentially relevant tasks, without
having access to the internals of individual predictors. We present a new
predictor combination algorithm that improves the target by i) measuring the
relevance of references based on their capabilities in predicting the target,
and ii) strengthening such estimated relevance. Unlike existing predictor
combination approaches that only exploit pairwise relationships between the
target and each reference, and thereby ignore potentially useful dependence
among references, our algorithm jointly assesses the relevance of all
references by adopting a Bayesian framework. This also offers a rigorous way to
automatically select only relevant references. Based on experiments on seven
real-world datasets from visual attribute ranking and multi-class
classification scenarios, we demonstrate that our algorithm offers a
significant performance gain and broadens the application range of existing
predictor combination approaches.
