The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Early History of Machine Learning
Open accessAbstract
Machine learning belongs to the crossroad of cybernetics (control science) and computer science. It is attracting recently an overwhelming interest, both of professionals and of the general public. In the talk a brief overview of the historical development of the machine learning field with a focus on the development of mathematical apparatus in its first decades is provided. A number of little-known facts published in hard to reach sources are presented.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“AI can also systematically encode bias into decisions.”
From Artificial Intelligence and Privacy (2024) · cites this paper as [11]
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Research works printed but not linked: 42
- Abramson, 1963, Pattern recognition and machine learning IEEE Transactions on Information Theory, V.:, 9
- Aizerman M.A. (1963). The problem of training an automaton to perform classification of input situations (pattern recognition), Theory Self-Adapt. Contr. Syst. Proc. IFAC Symp. 2nd 1963.
- Aizerman, 1964, Theoretical foundations of the potential function method in the problem of training automata to classify input situations, Automat, Remote Contr. (USSR), 25
- Aizerman, 1964, The probabilistic problem of training automata to recognize patterns and the potential function method, Automat, Remote Contr. (USSR), 25
- Belhumeur, 1997, Eigenfaces vs, Fisherfaces: recognition using class specific linear projection IEEE Transactions on Pattern Analysis and Machine Intelligence, V.:, 19
- Bongard, 1961, Simulation of the recognition process on a digital computing machine, Biophysics, 4
- Brady, 1989, Back propagation fails to separate where perceptrons succeed, IEEE Transactions on Circuits and Systems, 36, 665, 10.1109/31.31314 · DOI
- Braverman, 1962, The experiments with training a machine to recognize patterns, Automat, Remote Contr., 23
- Bregman, 1966, Relaxation method of finding a point common to some given convex sets and its use in solving optimization problems Doklady Akademii Nauk SSSR, 171, 1019
- Bregman, 1965, Use of consecutive projection for finding a common point of convex sets Doklady Akademii Nauk SSSR, 162, 487
- Bregman, 1967, The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming, USSR Computational Mathematics and Mathematical Physics, 7, 200, 10.1016/0041-5553(67)90040-7 · DOI
- Bristow, 2006, A Survey of Iterative Learning Control.”, IEEE Control Systems Magazine, 1, 96
- Bush, 1951, A mathematical model for simple learning, Psychological Review, 58, 313, 10.1037/h0054388 · DOI
- Dechter R. (1986) Learning while searching in constraint-satisfaction problems. AAAI-86 Proceedings, pp.179– 183.
- Dreyfus, 1990, ”Artificial Neural Networks, Back Propagation, and the Kelley-Bryson Gradient Procedure”, Journal of Guidance, Control, and Dynamics., 13, 926, 10.2514/3.25422 · DOI
- Feldbaum, 1960, Dual Control Theory, Automation and Remote Control, 21, 874
- Foote, 2017, A Brief History of Deep Learning, 7
- Gori, 1992, On the problem of local minima in backpropagation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 76, 10.1109/34.107014 · DOI
- Gubin, 1967, The method of projections for finding the common point of convex sets, USSR Computational Mathematics and Mathematical Physics, 7, 1, 10.1016/0041-5553(67)90113-9 · DOI
- Kharichev, 1973, New problem in pattern recognition, Autom. Remote Control, V.34, Is., 1, 98
- Kozinets, 1966, Use Of Electron Computers In Criminalistics For Differentiation Between Very Similar Handwritings, Doklady Akademii Nauk SSSR V., 167, 1008
- Marill, 1960, Statistical Recognition Functions and the Design of Pattern Recognizers, IRE Transactions on Electronic Computers, EC-9
- Mitchell, 1974, Finding The Point Of A Polyhedron Closest To The Origin, SIAM J Control, 12, 19, 10.1137/0312003 · DOI
- Nesterov, 1983, A method of solving a convex programming problem with convergence rate O(1/k2, Soviet Mathematics Doklady, 27, 372
- Novikoff, 1962, On convergence proofs on per-ceptrons, Symposium on the Mathematical Theory of Automata, Polytechnic Institute of Brooklyn, 12, 615
- Polyak, 1964, Some methods of speeding up the convergence of iteration methods, USSR Computational Mathematics and Mathematical Physics, 4, 1, 10.1016/0041-5553(64)90137-5 · DOI · Paper page
- Polyak, 1992, Acceleration of stochastic approximation by averaging, SIAM Journal on Control and Optimization, 30, 838, 10.1137/0330046 · DOI · Paper page
- Recht B. (2018) A Tour of Reinforcement Learning: The View from Continuous Control. arXiv:1806.09460v2 [math.OC] 10 Nov 2018. · arXiv
- Rosenblatt F. (1959). Two Theorems of Statistical Separability in the Perceptron. Symposium of the Mechanisation of Thought Processes. National Physical Laboratory, Teddington, UK, Nov. 1958,Vol I, H.M. Stationery Office, London.
- Rosenblatt, 1960, Perceptron Simulation Experiments, Proc. Inst. Radio Engineers V., 18, 301
- Tsypkin, 1966, Adaptation, Training And Self-Organization In Automatic Systems, Automation And Remote Control, V., 27, 16
- Vapnik, 1963, Recognition of Patterns with help of Generalized Portraits, Avtomat. i Tele-mekh., 24, 774
- Vapnik, 1964, On a class of perceptrons, Automat. Remote Control, 25, 103
- Wang H., and B. Raj (2017) On the Origin of Deep Learning arXiv:1702.07800v4 [cs.LG] 3 Mar 2017 · arXiv
- Werbos, 1990, Backpropagation through time: what it does and how to do it, Proceedings of the IEEE, 78, 1550, 10.1109/5.58337 · DOI · Paper page
- Widrow, 1959, Adaptive sampled-data systems, A statistical theory of adaptation, WESCON Conv. Rec., 3
- Widrow, B. (1961). Self-adaptive discrete systems, Theory Self Adapf. Contr. Syst. Proc. IFAC Symp. 1st 1961.
- Widrow, 1964, Pattern recognition and adaptive control, IEEE Trans, Appl. Ind., 83
- Widrow, 1990, 30 Years of Adaptive Neural Networks: Perceptron, Madaline, and Backprop-agation, Proceedings IEEE, V., 78
- Wright, 2009, Robust Face Recognition via Sparse Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence V.:, 31
- Yakubovich, 1966, Recurrent finitely convergent algorithms for solving systems of inequalities, Sov. Math. Doklady, 1966, 300
- Yakubovich V.A. On a method of adaptive control under conditions of great uncertainty. Prepr. 5th World Congress IFAC (Paris). 1972. V.37. N3. pp.1–6. · DOI
Cited by
Evaluation metrics and statistical tests for machine learning
Rainio, O., Teuho, J., & Klén, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-56706-x · Cited by 1,338
From explainable to interactive AI: A literature review on current trends in human-AI interaction
Raees, M., Meijerink, I., Lykourentzou, I., Khan, V.-J., & Papangelis, K. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103301 · Cited by 180
Artificial Intelligence and Privacy
Solove, D. J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4713111 · Cited by 33
Metadata
- Authors
- Fradkov, A. L.
- Venue
- IFAC-PapersOnLine
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 53
- Issue
- 2
- Pages
- 1385-1390
- ISSN
- 2405-8963 (ISSN-L)
- OpenAlex
- W3152894225
- Cited by
- 190
- References
- 3
- Citations by year
- 2026: 20 · 2025: 42 · 2024: 51 · 2023: 43 · 2022: 20 · 2021: 11 · 2020: 3
- FWCI
- 17.56
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- IFAC-PapersOnLine (publishedVersion)
- Funders
- Government Council on Grants, Russian Federation (08-08)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › History of Computing Technologies
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Cybernetics and Technology in Society
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Historical Studies and Socio-cultural Analysis
- Keywords
- history of machine learning, cybernetics, mathematical foundations of machine learning, history of computer science
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review