Alejandro Cervantes

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Nearest prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper, we first use the standard particle swarm optimizer (PSO)(More)
BACKGROUND Population exposure assessment methods that capture local-scale pollutant variability are needed for large-scale epidemiological studies and surveillance, policy, and regulatory purposes. Currently, such exposure methods are limited. METHODS We created 2006 national pollutant models for fine particulate matter [PM with aerodynamic diameter ≤(More)
Nearest Prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper we develop a new algorithm (called AMPSO), based on the(More)
This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its application to continuous classification problems as a Nearest Prototype (NP) classifier. In Nearest Prototype classifiers, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns(More)
This paper presents an application of particle swarm optimization (PSO) to continuous classification problems, using a Michigan approach. In this work, PSO is used to process training data to find a reduced set of prototypes to be used to classify the patterns, maintaining or increasing the accuracy of the nearest neighbor classifiers. The Michigan approach(More)
Introduction • Software projects usually involve many people and many resources that have to be efficiently managed – Poor planning à project failure – Goal: providing software engineers with an automatic tool for such a task • Optimization problem: assigning employees to tasks • Particularities – Several people assigned to tasks – Task cost estimation is(More)
—Learning from non-stationary data requires methods that are able to deal with a continuous stream of data instances, possibly of infinite size, where the class distributions are potentially drifting over time. For handling such datasets, we are proposing a new method that incrementally creates and adapts a network of prototypes for classifying complex data(More)
Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of tremendous importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multiobjective(More)