Self-Organizing Maps
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
From outliers to prototypes: Ordering data
Neurocomputing
Minimum spanning tree based one-class classifier
Neurocomputing
Time Series Clustering for Anomaly Detection Using Competitive Neural Networks
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
A classifier system for author recognition using synonym-based features
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Authorship attribution as a case of anomaly detection: A neural network model
International Journal of Hybrid Intelligent Systems
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Writers tend to express their ideas with different styles, defined with the so called firm or stylome, which is an abstraction of the general constraints and specific combinations of words within their language they decide to follow. Although capturing this style has proven to be very difficult, some advances have been achieved. Here, we present a novel system that is trained with texts from the same author, and is able to unveil some of its features, and to apply them to detect texts not written by the same author, or, at least, not written with the previously learned features. The system is an hybrid model based in self-organizing maps and in information-theoretic aspects. In the model, mutual information function of unknown texts are compared to the mutual information function of texts from a known author. If the distance between these two distributions exceeds a certain threshold, then the unknown text is from a different author, otherwise the authorship is the same. The decision threshold is obtained by the self-organizing map trained with the texts from the same author. We present results in authorship identification in several contexts including classic literature, journalism (political, economical, sports), and scientific divulgation.