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Artificial neural networks for online learning problems are often implemented with synaptic plasticity to achieve adaptive behaviour. A common problem is that the overall learning dynamics are emergent properties strongly dependent on the correct combination of neural architectures, plasticity rules and environmental features. Which complexity in architectures and learning rules is required to match...
This paper presents a novel interval type-2 fuzzy inference system with automatic learning for handling uncertainty, called the hierarchical type-2 neuro-fuzzy BSP model (T2-HNFB). This new model combines the paradigms of the type-2 fuzzy inference systems and neural networks with recursive partitioning techniques (BSP - Binary Space Partitioning). The model is able to automatically create and expand...
Nowadays, there are huge amount of data in the bioinformatics field where lots of software packages are required in order to correctly handle and dissect available information. In this situation, final users often need specific software combinations for achieving satisfying results. A typical situation is the need to import many large files that, after processing, are stored on different formats....
The self-adaptive model of a XCS-based ensemble machine solving data-mining tasks has been presented. The results of experiments have shown the ability of the architecture to adapt the parameters of single XCS: the mutation rate mu and the tournament size ts- separately and/or together.
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