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24 ПродажO’qituvchili o’qitish
![II bob. O’qituvchili o’qitish (Supervised learning)
Sun’iy intellekt uchun statistik usullar
Hozirgi kunda sun’iy intellekt algoritmlari va dasturlarini ishlab chiqishda
an’anaviy statistik usullardan foydalanilmoqda. Berilgan to’plamlar qiymatlari asosida
regressiya va chiqizli interpolyatsiya usullaridan foydalanib kelingan.
Quyida mavjud statistik usullar keltirilgan bo’lib, bular hozirgi kunda SI
algoritmlari va dasturlarini ishlab chiqishda keng foydalanib kelinmoqda:
◦ Regressiya
◦ Sinflashtirish
◦ Klasterlash
◦ Ehtimollar nazariyasi
◦ Qoidalar daraxti
Mashinali o’qitish
Mashinali o’qitish (Machine Learning) – bu kompyuterni dasturlashning
yangi yo’nalishi bo’lib, dasturning ma’lumotlarni o’qib olish va shu ma’lumotlar
asosida o’rganish xususiyatiga ega bo’lishidir.
[Mashinali o’qitish] kompyuterning ma’lumotlarni o’qib olish asosida
xususiyatlarni o’rganish qobiliyatidir - Arthur Samuel, 1959 A dastur bu E tajriba
asosida T masalani yecishda P samaradorlik o’lchovi bilan yaratiladi, va T masala
samaradorligi P orqali tekshiriladi va aniqligi E tajriba asosida amalga oshiriladi –
Tom Mitchell, 1997.](https://docx.uz/documents/4588e049-abf0-4f77-8b27-788dd10515b8/page_13.png?v=1)
![FOYDALANILGAN ADABIYOTLAR
1. Cybenko, G. 1989. Approximation by superpositions of a sigmoidal
function Mathematics of Control, Signals, and Systems, 2(4), 303–314.
2. Linnainmaa, Seppo (1970). The representation of the cumulative
rounding error of an algorithm as a Taylor expansion of the local rounding errors
(Masters) (in Finnish). University of Helsinki. pp. 6–7.
3. Kelley, Henry J. (1960). "Gradient theory of optimal flight paths". ARS
Journal. 30 (10): 947–954. doi:10.2514/8.5282.
4. Rosenblatt, Frank. x. Principles of Neurodynamics: Perceptrons and the
Theory of Brain Mechanisms. Spartan Books, Washington DC, 1961
5. Werbos, Paul (1982). "Applications of advances in nonlinear sensitivity
analysis" (PDF). System modeling and optimization. Springer. pp. 762–770. Archived
(PDF) from the original on 14 April 2016. Retrieved 2 July 2017.
6. Rumelhart, David E., Geoffrey E. Hinton, and R. J. Williams. "Learning
Internal Representations by Error Propagation". David E. Rumelhart, James L.
McClelland, and the PDP research group. (editors), Parallel distributed processing:
Explorations in the microstructure of cognition, Volume 1: Foundation. MIT Press,
1986.
7. Hastie, Trevor. Tibshirani, Robert. Friedman, Jerome. The Elements of
Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York,
NY, 2009.
8. Rosenblatt, Frank (1958). "The Perceptron: A Probabilistic Model For
Information Storage And Organization in the Brain". Psychological Review. 65 (6):
386–408. CiteSeerX 10.1.1.588.3775. doi:10.1037/h0042519. PMID 13602029.
S2CID 12781225.
9. Rosenblatt, Frank (1962). Principles of Neurodynamics. Spartan, New
York.
10. Schmidhuber, Juergen (2022). "Annotated History of Modern AI and Deep
Learning". arXiv:2212.11279 [cs.NE].](https://docx.uz/documents/4588e049-abf0-4f77-8b27-788dd10515b8/page_26.png?v=1)
O’QITUVCHILI O’QITISH