Titlebook: Artificial Neural Networks in Pattern Recognition; 6th IAPR TC 3 Intern Neamat Gayar,Friedhelm Schwenker,Cheng Suen Conference proceedings

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Robert Lickliter PhD,Lorraine E. Bahrick PhDion of useful data. In this work we propose a multiple-imputation-type framework for estimating the missing values of a time series. This framework is based on iterative and successive forward and backward forecasting of the missing values, and constructing ensembles of these forecasts. The iterativ

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F. Sharp,R. B. Fraser,R. D. B. Milner. Such objectives might be the induction of a large margin or the reduction of the number of involved features. The underlying concept class of linear classifiers is analyzed less frequently. It is implicitly assumed that all classifiers of this function class share the same common properties..In th

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https://doi.org/10.1007/978-3-030-00051-6the property to benefit from fuzzy labeled data in the training phase and can determine fuzzy memberships for input data. The algorithm can be considered as an extension of the traditional multi-class SVM for crisp labeled data, and it also extents the fuzzy SVM approach for fuzzy labeled training d

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E. Kastendieck,W. Künzel,A. Jensenn-site preferences is critically important in functional genomics and gene therapy studies. It has been found that the deformability property of the local DNA structure of the integration sites, called .., is of significant importance in the target-site selection process. We considered the .. profil

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