10 research outputs found

    A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks

    Get PDF
    info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT" Custode, L. L., Tecce, C. L., Bakurov, I., Castelli, M., Cioppa, A. D., & Vanneschi, L. (2020). A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks. In P. A. Castillo, J. L. Jiménez Laredo, & F. Fernández de Vega (Eds.), Applications of Evolutionary Computation - 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings (pp. 513-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12104 LNCS). Springer. https://doi.org/10.1007/978-3-030-43722-0_33In recent years neuroevolution has become a dynamic and rapidly growing research field. Interest in this discipline is motivated by the need to create ad-hoc networks, the topology and parameters of which are optimized, according to the particular problem at hand. Although neuroevolution-based techniques can contribute fundamentally to improving the performance of artificial neural networks (ANNs), they present a drawback, related to the massive amount of computational resources needed. This paper proposes a novel population-based framework, aimed at finding the optimal set of synaptic weights for ANNs. The proposed method partitions the weights of a given network and, using an optimization heuristic, trains one layer at each step while “freezing” the remaining weights. In the experimental study, particle swarm optimization (PSO) was used as the underlying optimizer within the framework and its performance was compared against the standard training (i.e., training that considers the whole set of weights) of the network with PSO and the backward propagation of the errors (backpropagation). Results show that the subsequent training of sub-spaces reduces training time, achieves better generalizability, and leads to the exhibition of smaller variance in the architectural aspects of the network.authorsversionpublishe

    A complete gradient clustering algorithm formed with kernel estimators

    No full text
    The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures. Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects resulting from possible modifications with respect to their primarily assigned optimal values. The proposed algorithm does not demand strict assumptions regarding the desired number of clusters, which allows the obtained number to be better suited to a real data structure. Moreover, a feature specific to it is the possibility to influence the proportion between the number of clusters in areas where data elements are dense as opposed to their sparse regions. Finally, the algorithm-by the detection of one-element clusters-allows identifying atypical elements, which enables their elimination or possible designation to bigger clusters, thus increasing the homogeneity of the data set

    Bayes sharpening of imprecise information

    No full text
    A complete algorithm is presented for the sharpening of imprecise information, based on the methodology of kernel estimators and the Bayes decision rule, including conditioning factors. The use of the Bayes rule with a nonsymmetrical loss function enables the inclusion of different results of an under- and overestimation of a sharp value (real number), as well as minimizing potential losses. A conditional approach allows to obtain a more precise result thanks to using information entered as the assumed (e.g. current) values of conditioning factors of continuous and/or binary types. The nonparametric methodology of statistical kernel estimators freed the investigated procedure from arbitrary assumptions concerning the forms of distributions characterizing both imprecise information and conditioning random variables. The concept presented here is universal and can be applied in a wide range of tasks in contemporary engineering, economics, and medicine

    Model-based clustering and classification with non-normal mixture distributions

    No full text
    Non-normal mixture distributions have received increasing attention in recent years. Finite mixtures of multivariate skew-symmetric distributions, in particular, the skew normal and skew -mixture models, are emerging as promising extensions to the traditional normal and -mixture models. Most of these parametric families of skew distributions are closely related, and can be classified into four forms under a recently proposed scheme, namely, the restricted, unrestricted, extended, and generalised forms. In this paper, we consider some of these existing proposals of multivariate non-normal mixture models and illustrate their practical use in several real applications. We first discuss the characterizations along with a brief account of some distributions belonging to the above classification scheme, then references for software implementation of EM-type algorithms for the estimation of the model parameters are given. We then compare the relative performance of restricted and unrestricted skew mixture models in clustering, discriminant analysis, and density estimation on six real datasets from flow cytometry, finance, and image analysis. We also compare the performance of mixtures of skew normal and -component distributions with other non-normal component distributions, including mixtures with multivariate normal-inverse-Gaussian distributions, shifted asymmetric Laplace distributions and generalized hyperbolic distributions
    corecore