基于径向基神经网络和强化学习的Agent驱动语义辨识器
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
摘要：
由于数字化文档的巨大可用性，以及数字化文档的本质和传播方式所带来的欺骗可能性的增加，作者归属问题的相关性不断增强。目前，作者归属在信息检索和分析中，在安全、信任和版权保护的背景下变得非常重要。这项工作提出了一种创新的多代理驱动的机器学习技术，已开发的作者归属。通过对常用词汇进行预处理和时间段相关分析，确定词汇在分析文本中重复出现频率的偏差参考水平，然后训练基于径向基神经网络（RBPNN）的分类器识别正确的作者。所提出的方法的主要优点在于语义分析的通用性，它可以应用于不同的上下文和词汇域，而不需要任何修改。此外，所提出的系统能够结合外部输入，这意味着调整分类器，然后通过连续学习强化进行自我调整。
英文摘要：
Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
