Paulo J. L. Adeodato

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Neural networks and logistic regression have been among the most widely used AI techniques in applications of pattern clussiftiution. MLK~ has been discrlssed about if there is any signzficunt d&erence in between them but much less has been actually done with real-world applications data (large scale) to help settle this mutter, with a few exceptions. This(More)
This paper presents a new method --- the Time-delay Added Evolutionary Forecasting (TAEF) method --- for time series prediction which performs an evolutionary search of the minimum necessary number of dimensions embedded in the problem for determining the characteristic phase space of the time series. The method proposed is inspired in F. Takens theorem and(More)
This paper introduces a pRAM (probabilistic RAM) node system for sequential pattern veriication. It includes a recurrent network trained with reinforcement learning based on the current state training strategy to generate the target for the reward/penalty signal. The main issues concerning the architecture's applicability to sequential pattern veriication(More)
A fundamental question in the field of artificial neural networks is what set of problems a given class of networks can perform (computability). Such a problem can be made less general, but no less important, by asking what these networks could learn by using a given training procedure (learnability). The basic purpose of this paper is to address the(More)
This brief generalizes the forecasting method that has been awarded first-place winner in the International Competition of Time Series Forecasting (ICTSF 2012). It is based on a short-term forecasting approach of multilayer perceptrons (MLP) ensembles, combined dynamically with a long-term forecasting. The main feature of this general approach is the(More)