Carlos Santa Cruz

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Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data sets. We propose a new feature selection method, named Quadratic Programming Feature Selection (QPFS), that reduces the task to a quadratic optimization problem. In order to limit(More)
In this work, we will introduce linear autoassociative neural (AN) network filters for the removal of additive noise from one-dimensional (1-D) time series. The AN network will have a (2 + 1) (2 + 1) architecture, and for fixed, we will show how to choose the optimal value and output coordinate from square error estimates between the AN filter outputs and(More)
This paper presents an online system for fraud detection of credit card operations based on a neural classifier. Since it is installed in a transactional hub for operation distribution, and not on a card-issuing institution, it acts solely on the information of the operation to be rated and of its immediate previous history, and not on historic databases of(More)
This paper presents a nonlinear supervised feature extraction algorithm that combines Fisher's criterion function with a preliminary perceptron-like nonlinear projection of vectors in pattern space. Its main motivation is to combine the approximation properties of multilayer perceptrons (MLP's) with the target free nature of Fisher's classical discriminant(More)
The relat ionship between the quality of state space reconst ruction and the accuracy in time series forecast ing is analyzed. The averaged scalar product of the dynamical system flowvectors has been used to give a degree of determinism to the selected state space reconst ruct ion. This value helps dist inguish between those regions of the state space where(More)