Titlebook: Bio-Inspired Systems: Computational and Ambient Intelligence; 10th International W Joan Cabestany,Francisco Sandoval,Juan M. Corchado Confe

卷發(fā)

Eric Linhart,Friedrich Hedtrichcomputational cost. This paper describes a novel method to overcome this problem. A set of normalization values associated to the most frequent .-gramsis pre-computed and the model is smoothed with lower .-gramconnectionist or statistical models. The proposed approach is favourably compared to stand

種屬關(guān)系

GEM

Lice692

https://doi.org/10.1007/978-3-658-16714-1asses. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this contribution is to analyse the performance of CO.RBFN, a cooperative-competitive evolutionary model for the design of RBFNs applied to cla

glans-penis

‘Divide the Dollar’ Using Voting by Vetotead of selecting a subset of variables with forward or backward feature selection procedures, we modify the loss function in such a way that the maximal variation for each covariate is minimized, resulting in models which have sparse dependence on the features. Experiments on artificial data illust

外露

Heinz Brestel,Adolf Freiherr von Kniggeet the index pair giving the maximum KKT violation and combines it with the updating heuristics of Smola and Sch?lkopf enforcing at each training iteration a . condition. In this work we shall present an alternative, much simpler procedure that selects the updating indices as those giving a maximum

CALL

Jahrbuch für Kapitalanleger 1999 consider a Radial Basis Function Network and a Multi-Layer Perceptron. We examine the metrics of data complexity known as . over a wide range of data sets built from real data, and try to extract behaviour patterns from the results. We obtain rules that describe both good or bad behaviours of the A

群島

Thomas Schwinger,Hans J?rg Burmeister proposed technique is most effective when the reliability of the model significantly varies in different areas of input space, as it often happens in many real-world problems, allowing the user to predict how reliable is a given model for each specific situation. Alternatively, the proposed techniq

出生

LITHE

Doris Immich,Christian Gremmelsand the sample covariance matrix around every input vector. This choice yields poor robustness for real input datasets. We propose to use the L1-median to estimate the local mean and covariance matrix with a low sensitivity to outliers. In addition to this, a smoothing phase is considered, which imp