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从解释中学习:有序超图的根内核

Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs
课程网址: http://videolectures.net/icml07_wachman_lfi/  
主讲教师: Gabriel Wachman
开课单位: 塔夫斯大学
开课时间: 2007-06-23
课程语种: 英语
中文简介:
本文提供了一个用于学习有序超图的内核,这是一种捕获关系数据的形式化,用于归纳逻辑编程(ILP)。内核在基于超图中的遍历计算相似度时概括了图形内核的先前方法。对具有挑战性的化学数据集的实验表明,该内核优于现有的ILP方法,并且与最先进的图形内核竞争。实验还表明,图形数据的编码可以显着影响性能,这一事实在核心方法之外是有用的。
课程简介: The paper presents a kernel for learning from ordered hypergraphs, a formalization that captures relational data as used in Inductive Logic Programming (ILP). The kernel generalizes previous approaches to graph kernels in calculating similarity based on walks in the hypergraph. Experiments on challenging chemical datasets demonstrate that the kernel outperforms existing ILP methods, and is competitive with state-of-the-art graph kernels. The experiments also demonstrate that the encoding of graph data can affect performance dramatically, a fact that can be useful beyond kernel methods.
关 键 词: 有序超图; 逻辑编程; 化学数据集
课程来源: 视频讲座网
最后编审: 2019-04-17:lxf
阅读次数: 52