Learning from networked examples in a k-partite graph

Yuyi Wang; Jan Ramon; Zheng-Chu Guo

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arXiv · 1306.0393

Learning from networked examples in a k-partite graph

Yuyi Wang·Jan Ramon·Zheng-Chu Guo

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Abstract

Many machine learning algorithms are based on the assumption that training examples are drawn independently. However, this assumption does not hold anymore when learning from a networked sample where two or more training examples may share common features. We propose an efficient weighting method for learning from networked examples and show the sample error bound which is better than previous work.

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Yuyi Wang, Jan Ramon, Zheng-Chu Guo. 2013-06-03. Learning from networked examples in a k-partite graph. https://arxiv.org/abs/1306.0393

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