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A network based on the Inverse Function Delayed (ID) model, which can recall a temporal sequence of patterns, is proposed. The classical problem, that the network is forced to make long distance jumps due to strong attractors that have to be isolated from each other, is solved by the introduction of the ID neuron. The ID neuron has negative resistance in its dynamics, which makes a gradual change from one attractor to another possible. Also a second version of the model with paired conventional and ID neurons is presented.

When coupling data mining (DM) and learning agents, one of the crucial challenges is the need for the Knowledge Extraction (KE) process to be lightweight enough so that even resource (e.g., memory, CPU etc.) constrained agents are able to extract knowledge. We propose the Stratified Ordered Selection (SOS) method for achieving lightweight KE using dynamic numerosity reduction of training examples. SOS allows for agents to retrieve differentsized training subsets based on available resources. The method employs ranking-based subset selection using a novel Level Order (LO) ranking scheme. We show representativeness of subsets selected using the proposed method, its noise tolerance nature and ability to preserve KE performance over different reduction levels. When compared to subset selection methods of the same category, the proposed method offers the best trade-off between cost, reduction and the ability to preserve performance.