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Published in:
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2018 | OriginalPaper | Chapter

6. Advances, the K-Contractive Map: A Supervised Version of Auto-CM

Authors : Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi

Published in: Artificial Adaptive Systems Using Auto Contractive Maps

Publisher: Springer International Publishing

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Abstract

This section is devoted to a more advanced type of Auto-CM that is supervised.

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previous chapter Dataset Transformations and Auto-CM
next chapter Comparison of Auto-CM to Various Other Data Understanding Approaches
Literature
1.
Chauvin, Y., and D.E. Rumelhart (eds.). 1995. Backpropagation: Theory, Architectures, and Applications, Lawrence Erlbaum Associates. New Jersey: Inc. Publishers.
2.
Buscema, M., V. Consonni, D. Ballabio, A. Mauri, G. Massini, M. Breda, and R. Todeschini. 2014. K-CM: A New Artificial Neural Network. Application to Supervised Pattern Recognition, Chemometrics and Intelligent Laboratory Systems 138: 110–119.CrossRef
3.
Hinton, G.E., S. Osindero, and Y.-W. Teh. 2006. A Fast Learning Algorithm for Deep Belief Nets. Neural Computation 18 (7): 1527–1554.MathSciNetCrossRefMATH
4.
5.
Larochelle, H., and Y. Bengio. 2008. Classification Using Discriminative Restricted Boltzmann Machines. In Proceedings of the 25-th International Conference on Machine Learning, Helsinki, Finland.
6.
Gironi, M., B. Borgiani, E. Farina, E. Mariani, C. Cursano, M. Alberoni, R. Nemni, G. Comi, M. Buscema, R. Furlan, and Enzo Grossi. 2015. A Global Immune Deficit in Alzheimer’s Disease and Mild Cognitive Impairment Disclosed by a Novel Data Mining Process. Journal of Alzheimer’s Disease 43 (2015): 1199–1213.
Metadata
Title
Advances, the K-Contractive Map: A Supervised Version of Auto-CM
Authors
Paolo Massimo Buscema
Giulia Massini
Marco Breda
Weldon A. Lodwick
Francis Newman
Masoud Asadi-Zeydabadi
Copyright Year
2018
Book
Artificial Adaptive Systems Using Auto Contractive Maps

Print ISBN: 978-3-319-75048-4

Electronic ISBN: 978-3-319-75049-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75049-1_6

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