Learning from an imprecise teacher : probabilistic and evidential approaches
Learning from an imprecise teacher : probabilistic and evidential approaches
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向不精确的老师学习：概率和证据方法
DOI：
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发表时间：
2015
期刊：
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通讯作者：
P. Smets
中科院分区：
文献类型：
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作者：
C. Ambroise;T. Denœux;G. Govaert;P. Smets
考虑了一类学习问题，其中训练样本的类别只有部分指定。描述了两种解决这类问题的方法：最大似然方法，其中假设了将不精确标签与真实类联系起来的概率模型；以及可传递信念模型方法，其依赖于非概率形式来表示和处理不精确信息。使用模拟数据集对这两种方法进行了实验比较。
A type of learning problem is considered, in which the class of training examples is only partially specified. Two approaches to such problems are described: the maximum likelihood approach, in which a probabilistic model relating the imprecise label to the true class is postulated, and the Transferable Belief Model approach, which relies on a non probabilistic formalism for representing and manipulating imprecise information. These two methods are compared experimentally using simulated data sets.