This paper presents an interpretable neural system—termed Evolving Long-term Cognitive Network—for pattern classification. The proposed model was inspired by Fuzzy Maps, which are recurrent networks for modeling and simulation. network architecture is comprised of two blocks: a input layer output layer. Network that gets unfolded in the same way as other networks, thus producing sort abstract h...

Efficient and interpretable classification of time series is an essential data mining task with many real-world applications. Recently several dictionary- shapelet-based methods have been proposed that employ contiguous subsequences fixed length. We extend pattern to efficiently enumerate long variable-length sequential patterns gaps. Additionally, we discover at multiple resolutions thereby co...