Abstract
Conventional pattern classification aims to improve classification accuracy for the whole dataset. In the time of Big Data, however, there are circumstances in which people may take interest only in those typical instances and other issues like scalability and efficiency take priority. Keeping these issues in mind, in this paper, we revisit the perceptron algorithm. While it is a linear model, we show that with proper objective functions, it can be transformed to a probabilistic learner. The evaluation is carried out with the well known Pima diabetes database. The experimental results indicate that the perceptron algorithm is comparable to other sophisticated sophisticated algorithms in terms of the criteria discussed in this paper.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley, New York
Bishop CM (ed) (2006) Pattern recognition and machine learning. Springer, Heidelberg
McMahan HB, Holt G, Sculley D (2013) Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1222–1230
Newman DJ, Hettich S, Blake CL, Merz CJ (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/_mlearn/MLRepository.html
Rosenblatt F (1957) The perceptron-a perceiving and recognizing automaton. Technical report 85-460-1, Cornell Aeronautical Laboratory
Hu T, Yu Y, Xiong J, Sung SY (2006) Maximum likelihood combination of multiple clusterings. Pattern Recogn Lett 27(13):1457–1464
Rasson JP, Granville V (1995) Multivariate discriminant analysis and maximum penalized likelihood density estimation. J R Stat Soc B 57:501–517
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39:1–38
Bottou L (2010) Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT, pp 177–186
King RD, Feng C, Sutherland A (1995) Statlog: comparison of classification algorithms on large real-world problems. Appl Artif Intell 9(3):289–333
Raiko T, Valpola H, LeCun Y (2012) Deep learning made easier by linear transformations in perceptrons. In: Proceedings of the international conference on artificial intelligence and statistics. pp 24–932
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Li, Y. (2016). Perceptron: An Old Folk Song Sung on a New Stage. In: Park, J., Jin, H., Jeong, YS., Khan, M. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 393. Springer, Singapore. https://doi.org/10.1007/978-981-10-1536-6_17
Download citation
DOI: https://doi.org/10.1007/978-981-10-1536-6_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1535-9
Online ISBN: 978-981-10-1536-6
eBook Packages: Computer ScienceComputer Science (R0)