Editing Perceptron (section)

=== Subsequent work ===
Rosenblatt continued working on perceptrons despite diminishing funding. The last attempt was Tobermory, built between 1961 and 1967, built for speech recognition.<ref>Rosenblatt, Frank (1962). “''[https://web.archive.org/web/20231230210135/https://apps.dtic.mil/sti/tr/pdf/AD0420696.pdf#page=163 A Description of the Tobermory Perceptron]''.” Cognitive Research Program. Report No. 4. Collected Technical Papers, Vol. 2. Edited by Frank Rosenblatt. Ithaca, NY: Cornell University.</ref> It occupied an entire room.<ref name=":7">Nagy, George. 1963. ''[https://web.archive.org/web/20231230204827/https://apps.dtic.mil/sti/trecms/pdf/AD0607459.pdf System and circuit designs for the Tobermory perceptron]''. Technical report number 5, Cognitive Systems Research Program, Cornell University, Ithaca New York.</ref> It had 4 layers with 12,000 weights implemented by toroidal [[magnetic core]]s. By the time of its completion, simulation on digital computers had become faster than purpose-built perceptron machines.<ref>Nagy, George. "Neural networks-then and now." ''IEEE Transactions on Neural Networks'' 2.2 (1991): 316-318.</ref> He died in a boating accident in 1971.
[[File:Isometric view of Tobermory Phase I.png|thumb|Isometric view of Tobermory Phase I.<ref name=":7" />]]
The [[kernel perceptron]] algorithm was already introduced in 1964 by Aizerman et al.<ref>{{cite journal |last1=Aizerman |first1=M. A. |last2=Braverman |first2=E. M. |last3=Rozonoer |first3=L. I. |year=1964 |title=Theoretical foundations of the potential function method in pattern recognition learning |journal=Automation and Remote Control |volume=25 |pages=821–837 }}</ref> Margin bounds guarantees were given for the Perceptron algorithm in the general non-separable case first by [[Yoav Freund|Freund]] and [[Robert Schapire|Schapire]] (1998),<ref name="largemargin">{{Cite journal |doi=10.1023/A:1007662407062 |year=1999 |title=Large margin classification using the perceptron algorithm |last1=Freund |first1=Y. |author-link1=Yoav Freund |journal=[[Machine Learning (journal)|Machine Learning]] |volume=37 |issue=3 |pages=277–296 |last2=Schapire |first2=R. E. |s2cid=5885617 |author-link2=Robert Schapire |url=http://cseweb.ucsd.edu/~yfreund/papers/LargeMarginsUsingPerceptron.pdf|doi-access=free }}</ref> and more recently by [[Mehryar Mohri|Mohri]] and Rostamizadeh (2013) who extend previous results and give new and more favorable L1 bounds.<ref>{{cite arXiv |last1=Mohri |first1=Mehryar |last2=Rostamizadeh |first2=Afshin |title=Perceptron Mistake Bounds |eprint=1305.0208 |year=2013 |class=cs.LG }}</ref><ref>[https://mitpress.mit.edu/books/foundations-machine-learning-second-edition] Foundations of Machine Learning, MIT Press (Chapter 8).</ref>

The perceptron is a simplified model of a biological [[neuron]]. While the complexity of [[biological neuron model]]s is often required to fully understand neural behavior, research suggests a perceptron-like linear model can produce some behavior seen in real neurons.<ref>{{cite journal |last1=Cash |first1=Sydney |first2=Rafael |last2=Yuste |title=Linear Summation of Excitatory Inputs by CA1 Pyramidal Neurons |journal=[[Neuron (journal)|Neuron]] |volume=22 |issue=2 |year=1999 |pages=383–394 |doi=10.1016/S0896-6273(00)81098-3 |pmid=10069343 |doi-access=free }}</ref>

The solution spaces of decision boundaries for all binary functions and learning behaviors are studied in.<ref>{{cite book |last1=Liou |first1=D.-R. |title=Learning Behaviors of Perceptron |last2=Liou |first2=J.-W. |last3=Liou |first3=C.-Y. |publisher=iConcept Press |year=2013 |isbn=978-1-477554-73-9}}</ref>