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
A survey of modern authorship attribution methods
Journal of the American Society for Information Science and Technology
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
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
A ranking-based algorithm for detection of outliers in categorical data
International Journal of Hybrid Intelligent Systems
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Writings by the same author usually share specific traits, the so-called stylome, which is defined as an abstraction of the constraints and specific sequences of words and phrases used in the texts. Although identifying a stylome has been elusive, some advancements in this area have been made. Here, we present a system trained with texts from a given author that then unveiled some of its features and, in turn, detected texts not written by that author, or written within a different style. The system is based on time series processing capabilities of an unsupervised neural network model known as the self-organizing map. The core idea is that a system trained with texts by one author should detect an anomaly when presented with texts from other authors. We present results of authorship identification in several contexts including known benchmarks as well as some examples from literature, journalism, and popular science.