Abstract
Linear separabilty of learning sets is a basic concept of neural networks theory. Exploration of the linear separability can be based on the minimization of the perceptron criterion function. Modification of the perceptron criterion function have been proposed recently aimed at feature selection problem. The modified criterion functions allows, among others, for discovering minimal feature subset that assure linear separability. Learning algorithm linked to the modified function is formulated in the paper.
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
Preview
Unable to display preview. Download preview PDF.
References
Rosenblatt, F.: Principles of neurodynamics. Spartan Books, Washington (1962)
Minsky, M.L., Papert, S.A.: Perceptrons - Expanded Edition. An Introduction to Computational Geometry. The MIT Press, Cambridge (1987)
Duda, O.R., Hart, P.E., Stork, D.G.: Pattern classification. J. Wiley, New York (2001)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1953); Rosenblatt F.: Principles of neurodynamics. Spartan Books, Washington (1962)
Bobrowski, L.: Eksploracja danych oparta na wypukłych i odcinkowo-liniowych funkcjach kryterialnych (Data mining based on convex and piecewise linear (CPL) criterion functions). Technical University Białystok (2005) (in Polish)
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. Intech (2011)
Bobrowski, L.: Design of piecewise linear classifiers from formal neurons by some basis exchange technique. Pattern Recognition 24(9), 863–870 (1991)
Kushner, H.J., Clark, D.S.: Stochastic Approximation for Constrained and Unconstrained Systems. Springer, Berlin (1978)
Wu, T.T., Chen, Y.F., Hastie, T., Sobel, E., Lange, K.: Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25(6), 714–721 (2009)
Vapnik, V.N.: Statistical Learning Theory. J. Wiley, New York (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bobrowski, L. (2013). CPL Criterion Functions and Learning Algorithms Linked to the Linear Separability Concept. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-41013-0_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41012-3
Online ISBN: 978-3-642-41013-0
eBook Packages: Computer ScienceComputer Science (R0)