Abstract
Designing classifiers on high-dimensional learning datasets is an important but difficult task in many artificial intelligence applications. Classifier design often involves learning algorithms of hierarchical neural networks.
Deep learning based on the backpropagation method is commonly used to learn hierarchical networks for classification tasks. In this approach, the multi-layer structure of the neural network is defined a priori. According to the proposed high learning, the structure of multi-layer classifiers results from learning data sets based on the principles of separable aggregation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bishop, C.M.: Pattern Recognition and Machine Learning. ISS. Springer, New York (2006)
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis. Prentice-Hall Inc., Englewood Cliffs, New York (2002)
Duda, O.R., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
Aggarwal, C.C.: Neural Networks and Deep Learning. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94463-0
Bobrowski, L.: Data Exploration and Linear Separability, pp. 1–172. Lambert Academic Publishing, Saarbrücken (2019)
Bobrowski L.: Computing on vertices in data mining. In: Data Mining, pp. 1–19. IntechOpen (2021)
Bobrowski, L.: Complex layers of formal neurons. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds.) Engineering Applications of Neural Networks, EANN 2022. CCIS, vol. 1600, pp. 81–89. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08223-8_7
Bobrowski, L., Łukaszuk, T.: Relaxed linear separability (RLS) approach to feature (Gene) subset selection. In: Xia, X. (ed.) Selected Works in Bioinformatics, pp. 103–118. IntechOpen (2011)
Bobrowski, L.: Induction of linear separability through the ranked layers of binary classifiers. In: Iliadis, L., Jayne, C. (eds.) Engineering Applications of Neural Networks, EANN AIAI 2011. IFIP Advances in Information and Communication Technology, vol. 363, pp. 69–77. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23957-1_8
Bobrowski, L., Boldak, C.: Stepwise inversion of large matrices with the Gauss-Jordan vector transformation. J. Adv. Math. Comput. Sci. 37(1), 28–39 (2022)
Holmgreen, L., Tirone, V., Gerhart, J., Hobfoll, S.E.: Conservation of Resources Theory. Wiley, New York (2017)
Acknowledgments
The presented study was supported by the grant WZ/WI-IIT/4/2023. From the Bialystok University of Technology and funded from the resources for research by the Polish Ministry of Science and Higher Education.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bobrowski, L. (2024). High Learning Hierarchical Neural Networks. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(), vol 14811. Springer, Cham. https://doi.org/10.1007/978-3-031-70819-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-70819-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70818-3
Online ISBN: 978-3-031-70819-0
eBook Packages: Computer ScienceComputer Science (R0)