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- Aristoklis D. Anastasiadis, George D. Magoulas, Michael N. Vrahatis
- Neurocomputing
- 2005

In this paper, a new globally convergent modification of the Resilient Propagation-Rprop algorithm is presented. This new addition to the Rprop family of methods builds on a mathematical framework for the convergence analysis that ensures that the adaptive local learning rates of the Rprop’s schedule generate a descent search direction at each iteration.… (More)

There are so many existing classification methods from diverse fields including statistics, machine learning and pattern recognition. New methods have been invented constantly that claim superior performance over classical methods. It has become increasingly difficult for practitioners to choose the right kind of the methods for their applications. So this… (More)

- Aristoklis D. Anastasiadis, George D. Magoulas, Michael N. Vrahatis
- Pattern Recognition Letters
- 2005

This paper introduces a new class of sign-based training algorithms for neural networks that combine the sign-based updates of the Rprop algorithm with the composite nonlinear Jacobi method. The theoretical foundations of the class are described and a heuristic Rprop-based Jacobi algorithm is empirically investigated through simulation experiments in… (More)

- George D. Magoulas, Aristoklis D. Anastasiadis
- I. J. Bifurcation and Chaos
- 2006

This paper explores the use of the nonextensive q-distribution in the context of adaptive stochastic searching. The proposed approach consists of generating the “probability” of moving from one point of the search space to another through a probability distribution characterized by the q entropic index of the nonextensive entropy. The potential benefits of… (More)

This paper introduces an efficient modification of the Rprop algorithm for training neural networks. The convergence of the new algorithm can be justified theoretically, and its performance is investigated empirically through simulation experiments using some pattern classification benchmarks. Numerical evidence shows that the algorithm exhibits improved… (More)

This paper introduces a new hybrid approach for learning systems that builds on the theory of nonextensive statistical mechanics. The proposed learning scheme uses only the sign of the gradient, and combines adaptive stepsize local searches with global search steps that make use of an annealing schedule inspired from nonextensive statistics, as proposed by… (More)

Training neural networks in classification problems, especially when biological data are involved, is a very challenging task. Many training algorithms have been proposed so far to improve the performance of neural networks. A popular approach is to use batch learning that employs a different adaptive learning rate for each weight. Most of the existing… (More)

- Aristoklis D. Anastasiadis, George D. Magoulas
- Neural Computing & Applications
- 2006

Scientists involved in the area of proteomics are currently seeking integrated, customised and validated research solutions to better expedite their work in proteomics analyses and drug discoveries. Some drugs and most of their cell targets are proteins, because proteins dictate biological phenotype. In this context, the automated analysis of protein… (More)

In this work we have studied the research activity for countries of Europe, Latin America and Africa for all sciences between 1945 and November 2008. All the data are captured from the Web of Science database during this period. The analysis of the experimental data shows that, within a nonextensive thermostatistical formalism, the Tsallis q-exponential… (More)

In this paper we propose an Rprop modification that builds on a mathematical framework for the convergence analysis to equip Rprop with a learning rates adaptation strategy that ensures the search direction is a descent one. Our analysis is supported by experiments illustrating how the new learning rates adaptation strategy works in the test cases to… (More)