Abstract
Optimization as a generic term is defined by the Merriam-Webster dictionary as: an act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as possible; specifically: the mathematical procedures (as finding the maximum of a function) involved in this.
God always takes the simplest way
Albert Einstein
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
References
A. Kruger, “Median-cut color quantization,” Dr. Dobb’s J., 46–54 and 91–92, Sept. (1994)
S. Smale, On the average number of steps of the simplex method of linear programming. Math. Program., 241–262 (1983)
Y. Chauvin, D.E. Rumelhart, Back Propagation: Theory, Architectures, and Applications (Lawrence Erlbaum Associates Publishers, UK, 1995)
G. Hammerly, C. Elkan, Alternatives to the k-means algorithm that find better clusterings, in Proceedings of the 11th ACM CIKM(2002), 600–607
D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, MA, 1989) pp. 1–25
S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing. Science 220, 671–680 (1983)
T. Back, F. Kursawe, Evolutionary algorithms for fuzzy logic: a brief overview, In Fuzzy Logic and Soft Computing, World Scientific (Singapore, 1995), pp. 3–10
U.M. Fayyad, G.P. Shapire, P. Smyth, R. Uthurusamy, Advances in Knowledge Discovery and Data Mining (MIT Press, Cambridge, 1996)
A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence (Wiley, New York, 2005)
J. Kennedy, R Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, vol. 4 (Perth, Australia, 1995), pp. 1942–1948
M.G. Omran, A. Salman, A.P. Engelbrecht, Particle Swarm Optimization for Pattern Recognition and Image Processing (Springer, Berlin, 2006)
M. Løvberg, T. Krink, Extending Particle Swarm Optimizers with Self-Organized Criticality. Proc. IEEE Congr. Evol. Comput. 2, 1588–1593 (2002)
M. Riedmiller, H. Braun, “A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm,” in Proceedings of the IEEE International Conference on Neural Networks (1993), pp. 586–591
G-J Qi, X-S Hua, Y. Rui, J. Tang, H.-J. Zhang, Image Classification With Kernelized Spatial-Context, IEEE Transactions on Multimedia 12(4), 278–287, June (2010). doi:10.1109/TMM.2010.2046270
K. Ersahin, B. Scheuchl, I. Cumming, Incorporating texture information into polarimetric radar classification using neural networks,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symp (Anchorage, USA, 2004), pp. 560–563
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kiranyaz, S., Ince, T., Gabbouj, M. (2014). Introduction. In: Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Adaptation, Learning, and Optimization, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37846-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-37846-1_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37845-4
Online ISBN: 978-3-642-37846-1
eBook Packages: EngineeringEngineering (R0)