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)
Training part-of-speech taggers (POS-taggers) requires iterative time-consuming convergence-dependable steps, which involve either expectation maximization or weight balancing processes, depending on whether the tagger uses stochastic or neural approaches, respectively. Due to the complexity of these steps, multilingual part-of-speech tagging can be an(More)
This paper introduces SATyrus, a neuro-symbolic architecture oriented to optimization problem solving via mapping problems specification into sets of pseudo-Boolean constraints. SATyrus provides a logical declarative language used to specify and compile a target problem into a particular energy function representing its space state of solutions. The(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)
One of the major data mining tasks is to cluster similar data, because of its usefulness, providing means of summarizing large ammounts of raw data into handy information. Clustering data streams is particularly challenging, because of the constraints imposed when dealing with this kind of input. Here we report our work, in which it was investigated the use(More)