Aristoklis D. Anastasiadis

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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)
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)
Scientists involved in the area of proteomics are currently seeking integrated, customised and validated research solutions to better expedite their work in pro-teomics 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 paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neural networks. The new algorithm combines deterministic and stochastic search steps by employing a different adaptive stepsize for each network weight, and applies a form of noise(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)
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)
This paper introduces a class of adaptive particle swarm optimization (PSO) methods that build on the theory of nonextensive statistical mechanics. These methods combine the traditional position update rule with an annealing schedule that is based on the nonextensive entropy. Comparative experiments conducted on benchmark functions, have showed that the(More)
This paper explores the use of the nonextensive q-distribution in the context of adaptive stochas-tic 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(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)
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