Robust variable length data classification with extended sequential fuzzy indexing tables
Annamária R. Várkonyi-Kóczy, Balázs Tusor, János T. Tóth · 2017
Recurrent Neural Networks are widely used tools for the classification of variable length data. However, their training is generally a very time-consuming task, especially for problems with high dimensions. The classification method proposed in this paper aims to provide a fast and simple alternative. Extended Sequential Fuzzy Indexing Tables are following the principle behind lookup table classifiers in that they realize an input-output association by mapping the problem space using arrays. The proposed network achieves this by breaking the multi-dimensional problem space down to a sequence of combinations, resulting in a flexible architecture that can work well with varying length data.