In this paper we show that Ullman and Basri’s linear combination (LC) representation, which was originally proposed for alignment-based object recognition, can be used for outli...
In this paper, the error-reject trade-off of linearly combined multiple classifiers is analysed in the framework of the minimum risk theory. Theoretical analysis described in [12,1...
In this paper, we propose a novel speaker adaptation technique, regularized-MLLR, for Computer Assisted Language Learning (CALL) systems. This method uses a linear combination of ...
Dean Luo, Yu Qiao, Nobuaki Minematsu, Yutaka Yamau...
In this paper, a framework for the analysis of the error-reject trade-off in linearly combined classifiers is proposed. We start from a framework developed by Tumer and Ghosh [1,2...
So far few theoretical works investigated the conditions under which specific fusion rules can work well, and a unifying framework for comparing rules of different complexity is cl...