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Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data

机译:通过自组织映射结构化数据学习非稀疏核

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摘要

The development of neural network (NN) models able to encode structured input, and the more recent definition of kernels for structures, makes it possible to directly apply machine learning approaches to generic structured data. However, the effectiveness of a kernel can depend on its sparsity with respect to a specific data set. In fact, the accuracy of a kernel method typically reduces as the kernel sparsity increases. The sparsity problem is particularly common in structured domains involving discrete variables which may take on many different values. In this paper, we explore this issue on two well-known kernels for trees, and propose to face it by recurring to self-organizing maps (SOMs) for structures. Specifically, we show that a suitable combination of the two approaches, obtained by defining a new class of kernels based on the activation map of a SOM for structures, can be effective in avoiding the sparsity problem and results in a system that can be significantly more accurate for categorization tasks on structured data. The effectiveness of the proposed approach is demonstrated experimentally on two relatively large corpora of XML formatted data and a data set of user sessions extracted from website logs.
机译:能够对结构化输入进行编码的神经网络(NN)模型的开发,以及对结构内核的最新定义,使得可以将机器学习方法直接应用于通用结构化数据。但是,内核的有效性可能取决于其相对于特定数据集的稀疏性。实际上,内核方法的准确性通常随着内核稀疏度的增加而降低。稀疏性问题在涉及离散变量的结构化域中尤其常见,离散变量可能具有许多不同的值。在本文中,我们在两个著名的树内核上探讨了这个问题,并建议通过重复使用结构的自组织映射(SOM)来解决这个问题。具体而言,我们表明,通过基于结构的SOM的激活图定义一类新的内核而获得的两种方法的适当组合可以有效避免稀疏性问题,并且可以使系统显着提高对结构化数据的分类任务准确无误。在两个相对较大的XML格式数据集和从网站日志中提取的用户会话数据集上,实验证明了该方法的有效性。

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