Binary Classification with a Pseudo Exponential Model and Its Application for Multi-Task Learning†
1
Future University Hakodate, 116-2 Kamedanakano, Hakodate Hokkaido 041-8655, Japan
2
The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Proceedings of the MaxEnt 2014 Conference on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Amboise, France, 21–26 September 2014.
Academic Editors: Frédéric Barbaresco and Ali Mohammad-Djafari
Entropy 2015, 17(8), 5673-5694; https://doi.org/10.3390/e17085673
Received: 11 May 2015 / Revised: 31 July 2015 / Accepted: 3 August 2015 / Published: 6 August 2015
(This article belongs to the Special Issue Information, Entropy and Their Geometric Structures)
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. View Full-Text
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MDPI and ACS Style
Takenouchi, T.; Komori, O.; Eguchi, S. Binary Classification with a Pseudo Exponential Model and Its Application for Multi-Task Learning. Entropy 2015, 17, 5673-5694.