Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Agmon S (1954) The relaxation method for linear inequalities. Can J Math 6(3):382–392
Block HD (1962) The perceptron: a model for brain functioning. Rev Mod Phys 34:123–135
Blum A, Dunagan JD (2002) Smoothed analysis of the perceptron algorithm for linear programming. In: Proceedings of the thirteenth annual symposium on discrete algorithms, San Francisco
Cesa-Bianchi N, Gentile C (2006) Tracking the best hyperplane with a simple budget perceptron. In: Proceedings of the nineteenth annual conference on computational learning theory, Pittsburgh
Collins M (2002) Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In: Conference on empirical methods in natural language processing, Philadelphia
Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive aggressive algorithms. J Mach Learn Res 7:551–585
Crammer K, Singer Y (2002) A new family of online algorithms for category ranking. In: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval, Tampere
Dekel O, Shalev-Shwartz S, Singer Y (2005) The Forgetron: a kernel-based perceptron on a fixed budget. Adv neural Inf Process Syst 18: 259–266
Freund Y, Schapire RE (1998) Large margin classification using the perceptron algorithm. In: Proceedings of the eleventh annual conference on computational learning theory, Madison
Gentile C (2002) The robustness of the p-norm algorithms. Mach Learn 53(3), 265–299
Minsky M, Papert S (1969) Perceptrons: an introduction to computational geometry. MIT, Cambridge
Novikoff ABJ (1962) On convergence proofs on perceptrons. In: Proceedings of the symposium on the mathematical theory of automata, New York, vol XII, pp 615–622
Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65:386–407
Shalev-Shwartz S, Singer Y (2005) A new perspective on an old perceptron algorithm. In: Proceedings of the eighteenth annual conference on computational learning theory, Bertinoro, 264–278
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this entry
Cite this entry
Shalev-Shwartz, S. (2016). Perceptron Algorithm. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2864-4_287
Download citation
DOI: https://doi.org/10.1007/978-1-4939-2864-4_287
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-2863-7
Online ISBN: 978-1-4939-2864-4
eBook Packages: Computer ScienceReference Module Computer Science and Engineering