Priscila Machado Vieira Lima

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Mimicking biological neurons by focusing on the excitatory/inhibitory decoding performed by the dendritic trees is a different and attractive alternative to the integrate-and-fire McCullogh-Pitts neuron stylisation. In such alternative analogy, neurons can be seen as a set of RAM nodes addressed by Boolean inputs and producing Boolean outputs. The(More)
Datasets with a large amount of noisy data are quite common in real-world classification problems. Robustness is an important characteristic of state-of-the-art classifiers that use error minimization techniques, thus requiring a long time to converge. This paper presents ClusWiSARD, a clustering customization of the WiSARD weightless neu-ral network model,(More)
Weightless neural networks constitute a still not fully explored Machine Learning paradigm, even if its first model, WiSARD, is considered. Bleaching, an improvement on WiSARD's learning mechanism was recently proposed in order to avoid overtraining. Although presenting very good results in different application domains, the original sequential bleaching(More)
This paper evaluates the WiSARD weightless model as a classification system on the problem of tracking multiple objects in real-time. Exploring the structure of this model, the proposed solution applies a re-learning stage in order to avoid interferences caused by background noise or variations in the target shape. Once the tracker finds a target at the(More)
Random Access Memory (RAM) nodes can play the role of artificial neurons that are addressed by Boolean inputs and produce Boolean outputs. The weightless neural network (WNN) approach has an implicit inspiration in the decoding process observed in the dendritic trees of biological neurons. An overview on recent advances in weightless neural systems is(More)