General multi-view learning with maximum entropy discrimination
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Mar 7, 2019 · MED [1] is a learning framework for discriminative estimation which combines the principles of the maximum entropy and large margin. It ...
Maximum entropy discrimination (MED) is a gen- eral framework for discriminative estimation based on the well known maximum entropy principle, which embodies ...
In this paper, we extend AMvMED and MkMvMED to the general multi-view classification problems by jointly learning multiple different views in a non-pairwise way ...
Maximum entropy discrimination (MED) is a general framework for discriminative estimation based on the well known maximum entropy principle, which embodies ...
In this paper, we present a multi-view maximum entropy discrimination framework that is an extension of MED to the scenario of learning with multiple feature ...
In this paper, we extend AMvMED and MkMvMED to the general multi-view classification problems by jointly learning multiple different views in a non-pairwise way ...
This paper proposes a new method to make use of the distinct views where classification margins from these views are required to be identical, and gives the ...
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Maximum entropy discrimination (MED) is a general framework for discriminative estimation which integrates the principles of maximum entropy and maximum margin.
In this paper, we extend AMvMED and MkMvMED to the general multi-view classification problems by jointly learning multiple different views in a non-pairwise way ...
在本文中，我们通过以非成对方式联合学习多个不同视图，将AMvMED 和MkMvMED 扩展到一般多视图分类问题，称为通用替代多视图最大熵鉴别（GAMvMED）和通用多核多视图最大熵 ...