22nd EANN 2021, 25 - 27 June 2021, Greece

Repeatable functionalities in complex layers of formal neurons

Leon Bobrowski, Tomasz Ɓukaszuk

Abstract:

  Complex layers of formal neurons can be designed on data sets built from a relatively small number of multidimensional feature vectors. Data sets with this structure can almost always be separated linearly. Genetic data sets typically have this property. Maximizing the margins is a fundamental principle when designing linear classifiers (formal neurons) based on training sets consisting of a small number of multivariate feature vectors. Maximizing Euclidean (L2) margins is a basic concept in the support vector machines (SVM) method of classifiers designing. An alternative approach to designing formal neurons may be to maximize the margins based on the L1 norm. The margins of the L1 norm enable the design of complex layers of formal neurons  

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