Abstract
In this paper we compare two approaches to the estimation of parameters describing linear classifiers considering a feature selection possibility for multidimensional data. The parameters are determined by minimization of convex and piecewise linear penalty functionals. Optimization is performed using a base exchange strategies. We assess numerical efficiency, classification accuracy, and the dimension of the subspaces containing the found solutions.
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
Bobrowski L (1991) Design of piecewise linear classifiers from formal neurons by some basis exchange technique, Pattern Recognition, 24(9), pp. 863–870, 1991.
Bobrowski L(2005) Exploratory data analysis based on convex and piecewise linear (CPL) criterion functions (in Polish), Technical University Bialystok, 2005.
Burges CJC(1998) A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery 2:121–167, 1998.
Chang CC, Lin CJ(2001) Training nu-support vector classifiers: Theory and algorithms, Neural Computation, 13: 2119–2147, 2001.
Topczewska M, Lukaszuk T, Bobrowski L(2006) Modelling of long data sets with examples of applications (in Polish), XIII Workshop of Polish Society of Computer Simulation, 2006.
Vapnik VN(1998) Statistical Learning Theory. J. Wiley, New York, 1998.
Yu H and Yang J and Han J(2003), Classifying large data sets using SVMs with hierarchical clusters. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Topczewska, M. (2007). Pattern Classification Using Efficient Linear Classifiers with Small Number of Weights. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-75175-5_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75174-8
Online ISBN: 978-3-540-75175-5
eBook Packages: EngineeringEngineering (R0)