Abstract
In this paper, we investigate the basic properties of binary classification with a pseudo model based on the Itakura–Saito distance and reveal that the Itakura–Saito distance is a unique appropriate measure for estimation with the pseudo model in the framework of general Bregman divergence. Furthermore, we propose a novelmulti-task learning algorithm based on the pseudo model in the framework of the ensemble learning method. We focus on a specific setting of the multi-task learning for binary classification problems. The set of features is assumed to be common among all tasks, which are our targets of performance improvement. We consider a situation where the shared structures among the dataset are represented by divergence between underlying distributions associated with multiple tasks. We discuss statistical properties of the proposed method and investigate the validity of the proposed method with numerical experiments.
Highlights
In the framework of multi-task learning problems, we assume that there are multiple related tasks sharing a common structure and can utilize the shared structure to improve the generalization performance of classifiers for multiple tasks [1,2]
Banerjee et al [13] showed that there exists a unique Bregman divergence corresponding to every regular exponential family, and the Itakura–Saito distance is associated with the exponential distribution
We reveal the characterization of the Itakura–Saito distance for estimation with the pseudo model Equation (3) and the Bregman U-divergence
Summary
In the framework of multi-task learning problems, we assume that there are multiple related tasks (datasets) sharing a common structure and can utilize the shared structure to improve the generalization performance of classifiers for multiple tasks [1,2]. Most methods utilize the similarity among tasks to improve the performance of classifiers by representing the shared structure as a regularization term [3,4]. We tackle this problem using a boosting method, which makes it possible to adaptively learn complicated problems with low computational cost.
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