Supervised Learning by Training on Aggregate Outputs
Supervised Learning by Training on Aggregate Outputs
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DOI：
10.1109/icdm.2007.50
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发表时间：
2007-10
期刊：
影响因子：
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通讯作者：
D. Musicant;J. Christensen;Jamie F. Olson
中科院分区：
文献类型：
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作者：
D. Musicant;J. Christensen;Jamie F. Olson
监督学习是一个经典的数据挖掘问题，人们希望能够预测与特定输入向量相关联的输出值。我们提出了一个新的扭曲这个经典的问题，而不是有一个单独的输出值的训练集包含每个输入向量，在训练集中的输出值只给出了在一些输入向量的聚合。这个新问题产生于对质谱数据学习的特殊需求，但可以很容易地应用于数据被聚合以保持隐私的情况。我们提供了一个正式的描述，这个新的问题，分类和回归。然后，我们研究如何k-最近邻，神经网络和支持向量机可以适应这个问题。
Supervised learning is a classic data mining problem where one wishes to be be able to predict an output value associated with a particular input vector. We present a new twist on this classic problem where, instead of having the training set contain an individual output value for each input vector, the output values in the training set are only given in aggregate over a number of input vectors. This new problem arose from a particular need in learning on mass spectrometry data, but could easily apply to situations when data has been aggregated in order to maintain privacy. We provide a formal description of this new problem for both classification and regression. We then examine how k-nearest neighbor, neural networks, and support vector machines can be adapted for this problem.