Abstract
Data sets consisting of a relatively small number of high-dimensional feature vectors often appear, e.g. in bioinformatics preoblems. This data structure complicates the design of classification or regression models.
Complex layers of formal neurons (linear classifiers) can be designed on the basis of data sets composed of high-dimensional feature vectors. Linear classifiers of a given complex layer are designed on disjoint subsets of features obtained as a result of well-conditioned clustering. This feature clustering technique is related to matrix regularization.
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Acknowledgments
The presented study was supported by the grant WZ/WI-IIT/3/2020 from the Bialystok University of Technology and funded from the resources for research by the Polish Ministry of Science and Higher Education.
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Bobrowski, L. (2022). Complex Layers of Formal Neurons. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_7
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DOI: https://doi.org/10.1007/978-3-031-08223-8_7
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