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- Annalisa Riccardi, Francisco Fernández-Navarro, Sante Carloni
- IEEE Trans. Cybernetics
- 2014

In this paper, the well known stagewise additive modeling using a multiclass exponential (SAMME) boosting algorithm is extended to address problems where there exists a natural order in the targets using a cost-sensitive approach. The proposed ensemble model uses an extreme learning machine (ELM) model as a base classifier (with the Gaussian kernel and the… (More)

- Francisco Fernández-Navarro, Annalisa Riccardi, Sante Carloni
- IEEE Transactions on Neural Networks and Learning…
- 2014

Ordinal regression (OR) is an important branch of supervised learning in between the multiclass classification and regression. In this paper, the traditional classification scheme of neural network is adapted to learn ordinal ranks. The model proposed imposes monotonicity constraints on the weights connecting the hidden layer with the output layer. To do… (More)

This investigation considers the optimization of multiple gravity assist capture trajectories in the Jupiter system combining the well known Differential Evolution algorithm with different classes of constraint handling techniques. The trajectories are designed to reach a desired target orbit around Jupiter with minimum fuel consumption while satisfying… (More)

- Francisco Fernández-Navarro, Annalisa Riccardi, Sante Carloni
- IEEE Trans. Cybernetics
- 2015

This paper introduces a new instance-based algorithm for multiclass classification problems where the classes have a natural order. The proposed algorithm extends the state-of-the-art gravitational models by generalizing the scaling behavior of the class-pattern interaction force. Like the other gravitational models, the proposed algorithm classifies new… (More)

- Adiel Castaño, Francisco Fernández-Navarro, Annalisa Riccardi, César Hervás-Martínez
- Neural Computing and Applications
- 2015

In the majority of traditional extreme learning machine (ELM) approaches, the parameters of the basis functions are randomly generated and do not need to be tuned, while the weights connecting the hidden layer to the output layer are analytically estimated. The determination of the optimal number of basis functions to be included in the hidden layer is… (More)

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