Germano C. Vasconcelos

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The reliability of feedforward neural networks with respect to the rejection of patterns not belonging to the deened training classes is investigated. It is shown how networks with diierent activation functions and propagation rules construct the decision regions in the pattern space and, therefore, aaect the network's performance in dealing with spurious(More)
Multilayer perceptron networks (MLP) are investigated with respect to the problem of the detection of spurious patterns. A novel mechanism is proposed based on the ideas of boot-strapping that when incorporated into the standard MLP provides the network with the ability to continuously modifying its responses across the input space. The mechanism makes use(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)
abstract This article presents an efficient solution for the PAKDD-2007 Competition cross-selling problem. The solution is based on a thorough approach which involves the creation of new input variables, efficient data preparation and transformation, adequate data sampling strategy and a combination of two of the most robust modeling techniques. Due to the(More)
Vehicle routing problems have been extensively analyzed to reduce transportation costs. More particularly, the vehicle routing problem with time windows (VRPTW) imposes the period of time of customer availability as a constraint, a very common characteristic in real world situations. Using minimization of the total distance as the main objective to be(More)
This work introduces a new method for time series prediction - time-delay added evolutionary forecasting (TAEF) - that carries out an evolutionary search of the minimum necessary time lags embedded in the problem for determining the phase space that generates the time series. The method proposed consists of a hybrid model composed of an artificial neural(More)
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)