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The implementation of new methods for reliable and fast identification and classification of seeds is of major technical and economical importance in the agricultural industry. As in ocular inspection, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work, we assess the discriminating power… (More)

The analysis and classification of seeds are essential activities contributing to the final added value in the crop production. Besides varietal identification and cereal grain grading, it is also of interest in the agricultural industry the early identification of weeds from the analysis of strange seeds, with the purpose of chemically controlling their… (More)

- Hugo D. Navone, Pablo M. Granitto, Pablo F. Verdes, H. Alejandro Ceccatto
- Inteligencia Artificial, Revista Iberoamericana…
- 2001

The performance of a single regressor/classifier can be improved by combining the outputs of several predictors. This is true provided the combined predictors are accurate and diverse enough, which posses the problem of generating suitable aggregate members in order to have optimal generalization capabilities. We propose here a new method for selecting… (More)

- P F Verdes, P M Granitto, H D Navone, H A Ceccatto
- Physical review letters
- 2001

We propose a simple method for the accurate reconstruction of slowly changing external forces acting on nonlinear dynamical systems. The method traces the evolution of the external force by locally linearizing the map dependency with the shifting parameter. Application of our algorithm to synthetic data corresponding to discrete models of evolving… (More)

The Kernel-Adatron (KA) algorithm was recently introduced as an alternative to quadratic programming for training support vector machines. In this paper we investigate three variants of the original KA algorithm and demonstrate through examples that they deliver excellent accuracy. The examples also show that one of these variants has rather different… (More)

Ensembles of artificial neural networks have been used in the last years as classification/regression machines, showing improved generalization capabilities that outperform those of single networks. However, it has been recognized that for aggregation to be effective the individual networks must be as accurate and diverse as possible. An important problem… (More)

- Pablo M. Granitto, Pablo F. Verdes, Hugo D. Navone, H. Alejandro Ceccatto
- Int. J. Neural Syst.
- 2001

Ensembles of artificial neural networks have been used in the last years as classification/regression machines, showing improved generalization capabilities that outperform those of single networks. However, it has been recognized that for aggregation to be effective the individual networks must be as accurate and diverse as possible. An important problem… (More)

How to generate and aggregate base learners to have optimal ensemble generalization capabilities is an important questions in building composite regression/classification machines. We present here an evaluation of several algorithms for artificial neural networks aggregation in the regression settings, including new proposals and comparing them with… (More)

- R. A. Calvo, H. D. Navone
- 2007

Artificial neural networks are parallel computational algorithms which can simulate very efficiently complex dynamical systems. In this work we show, by means of real-world applications, that this technique can be a useful tool in the analysis of time-series data related to climate. We study two time series: the first one characterizes the solar activity as… (More)

In several previous investigations, we presented models of triaxial stellar systems, both cuspy and non-cuspy, that were highly stable and harboured large fractions of chaotic orbits. All our models had been obtained through cold collapses of initially spherical N-body systems, a method that necessarily results in models with strongly radial velocity… (More)