Title :
Multilayered feedforward neural networks as a tool for distinction of the authors of texts
Author :
Selman, Suvad ; Husagic-Selman, Alma
Author_Institution :
Fac. of Eng. & Natural Sci., Int. Univ. of Sarajevo, Sarajevo, Bosnia-Herzegovina
Abstract :
This paper proposes a means of using a multilayered feedforward neural network to identify the author of a text. The network has to be trained where multilayer feedforward neural network as a powerful scheme for learning complex input-output mapping have been used in learning of the textual descriptors in a paragraphs of an author. The resulting training information we get will be used to identify the texts written by authors. The computational complexity is solved by dividing it into a number of computationally simple tasks where the input space is divided into a set of subspaces and then combining the solutions to those tasks. By this, we have been able to successfully distinguish the books authored by Leo Tolstoy, from the ones authored by Fyodor Dostoyevsky.
Keywords :
computational complexity; learning (artificial intelligence); multilayer perceptrons; pattern classification; text analysis; author identification; classification problem; complex input-output mapping; computational complexity; machine learning; multilayered feedforward neural network; textual descriptor; Accuracy; Biological neural networks; Multilayer perceptrons; Neurons; Testing; Training; Machine learning; artificial neural networks; author identification;
Conference_Titel :
Information, Communication and Automation Technologies (ICAT), 2011 XXIII International Symposium on
Conference_Location :
Sarajevo
Print_ISBN :
978-1-4577-0744-5
DOI :
10.1109/ICAT.2011.6102108