一种基于动态协同神经网络的文本作者身份分类研究

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 143-145.

一种基于动态协同神经网络的文本作者身份分类研究

张爱华

军事经济学院武汉430035

出版日期:2018-11-14 发布日期:2018-11-14

Dynamic Synergetic Neural Network Algorithm for Authorship Classification of Texts

ZHANG Ai-hua

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 为了提高文本作者身份分类的准确度,提出一种动态协同神经网络算法。该算法利用了协同神经网络训练速度快、抗造声强等特点,并采取了注意参数动态调整的策略。通过原型模式向量与实验模式向量间的相似性动态地选取合适的注意参数,在演化过程中对误识别的模式进行自适应纠正。与平衡注意参数条件下的识别效果进行对比校验,结果表明,该算法在很大程度上提高了网络的自学习能力,从而改善了作者身份分类的精度和鲁棒性。

Abstract: To improve the accuracy of authorship classification of texts,a dynamic synergetic neural network algorithm was proposed in this study.Specifically,this algorithm makes use of the characteristics of fast training and strong noise-immunity,and dynamically adjusts the attention parameters.Thus,the initial mis-identified patterns are adaptively corrected by measuring similarity between the prototype pattern and the testing pattern in evolution process.Compared with classification result under balanced attention parameter,the experimental result demonstrates that self-learning ability of the network is significantly improved in the dynamic synergetic neural network algorithm,thus the classification performance and robustness are improved.

Key words: Synergetic neural network,Authorship classification,Attention parameter,Order parameter

张爱华. 一种基于动态协同神经网络的文本作者身份分类研究[J]. 计算机科学, 2015, 42(Z6): 143-145. https://doi.org/

ZHANG Ai-hua. Dynamic Synergetic Neural Network Algorithm for Authorship Classification of Texts[J]. Computer Science, 2015, 42(Z6): 143-145. https://doi.org/

参考文献

[1] Narayanan A,Paskov H S,Gong N Z,et al.,On the feasibility of internet-scale author identification[C]∥ Proc.of the IEEE Symposium on Security & Privacy(IEEE S&P).California,USA,2012:300-314
[2] Iqbal F,Binsalleeh H,Fung B C M,et al.A Unified Data Mining Solution for Authorship Analysis in Anonymous Textua Communications[J].Information Sciences,2011,231:98-112
[3] Houvardas J,Stamatatos E.N-gram Feature Selection for Authorship Identification[C]∥Proc.of the 12th International Conference on Artificial Intelligence:Methodology,Systems,Applications.[S.l.]:Springer,2006
[4] Burrows J F.Word patterns and story shapes:The statistical analysis of narrative style[J].Literary and Linguistic Computing,1992,2:6-67
[5] Li Jie-xun,Zheng Rong,Chen Hsin-Chun.From Fingerprint to Writeprint[J].Communications of the ACM,2006,49(4):76-82
[6] 孙建文,刘三牙,杨宗凯,等.采用集成特征选择的网络书写纹识别研究[J].小型微型计算机系统,2012,3(5):1108-1112
[7] 刘三女牙,铁璐,刘智,等.基于多元概率推理模型的中文书写纹识别[J].计算机工程,2013,39(11):158-162
[8] Haken H.Synergetics[M].Springer-Verlag Press,1977
[9] Liu M,Fu Y.On Synergetic Theory and Application in Sports Curriculum Reform[C]∥Proc.of the 2009 First International Workshop on Education Technology and Computer Science.2009:557-562
[10] Wagner T,Boebel F G.Testing Synergetic Algorithms with Industrial Classification Problems[J].Neural Networks.1994,7(8):1313-1321
[11] http://www.amazon.com,2014

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