@inproceedings{6b511557d5dd4ee0b01896b8dc1d7108,
title = "A novel architecture for the classification and visualization of sequential data",
abstract = "This paper1 introduces a novel architecture to efficiently code in a self-organized manner, data from sequences or a hierarchy of sequences. The main objective of the architecture proposed is to achieve an inductive model of the sequential data through a learning algorithm in a finite vector space with generalization and prediction properties improved by the compression process. The architecture consists of a hierarchy of recurrent self-organized maps with emergence which performs a fractal codification of the sequences. An adaptive outlier detection algorithm is used to automatically extract the emergent properties of the maps. A visualization technique to help the analysis and interpretation of data is also developed. Experiments and results for the architecture are shown for an anomaly intrusion detection problem.",
author = "Jorge Couchet and Enrique Ferreira and Andr{\'e} Fonseca and Daniel Manrique",
year = "2007",
doi = "10.1007/978-3-540-71618-1_81",
language = "Ingl{\'e}s",
isbn = "9783540715894",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 1",
pages = "730--738",
booktitle = "Adaptive and Natural Computing Algorithms - 8th International Conference, ICANNGA 2007, Proceedings",
edition = "PART 1",
note = "8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007 ; Conference date: 11-04-2007 Through 14-04-2007",
}