The nature of statistical learning theory
The nature of statistical learning theory
Modern Information Retrieval
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Combining one-class classifiers for robust novelty detection in gene expression data
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
A novel approach for distributed application scheduling based on prediction of communication events
Future Generation Computer Systems
Extraction and classification of user behavior
EUC'07 Proceedings of the 2007 international conference on Embedded and ubiquitous computing
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
Energy-based function to evaluate data stream clustering
Advances in Data Analysis and Classification
Review: A review of novelty detection
Signal Processing
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In order to detect new events, a system must support on-line learning, adapting to pattern dynamic characteristics. Studies of such adaptation have originated the novelty detection area, which aims at identifying unexpected or unknown patterns. These researches have motivated this work to propose the on-line and unsupervised Self-Organizing Novelty Detection (SONDE) neural network. In this network, the creation of new neurons points out novelties. Experiments evaluated the influence of SONDE parameters and their capability to detect novelty events. These evaluations considered the datasets Biomed, ALL-AML Leukemia and DLBCL. Results are compared to others from GWR.