通过增强联合可预测性组合任务预测器
Combining Task Predictors via Enhancing Joint Predictability
摘要：
预测器组合旨在基于潜在相关任务的（参考）预测器来改进学习任务的（目标）预测器，而无需访问单个预测器的内部。我们提出了一种新的预测器组合算法，通过i）基于参考文献预测目标的能力来测量参考文献的相关性，以及ii）加强这种估计的相关性来改进目标。与现有的预测组合方法，只利用目标和每个参考之间的成对关系，从而忽略潜在的有用的参考之间的依赖，我们的算法联合评估的相关性，通过采用贝叶斯框架的所有参考。这也提供了一种严格的方法来自动选择相关的引用。基于视觉属性排序和多类分类场景的七个真实数据集的实验，我们证明了我们的算法提供了显着的性能增益，并拓宽了现有预测器组合方法的应用范围。
英文摘要：
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.
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