AJRATUVCHI GIPERTEKISLIKLAR VA CHIZIQLI QAROR QABUL QILUVCHI QOIDASI
Keywords:
ajratuvchi gipertekisliklar, chiziqli qaror qabul qiluvchi qoida, mashinani o'rganish, sun'iy intellekt, geometrik tahlil, model optimallashtirish, klassifikatsiya, qaror qabul qilish, ma'lumotlar tahlili, amaliy ilovalarAbstract
Ushbu maqola, sun'iy intellekt va mashinani o'rganish sohalarida keng qo'llaniladigan ajratuvchi gipertekisliklar va chiziqli qaror qabul qiluvchi qoidalar haqida batafsil ma'lumot beradi. Maqola, ajratuvchi gipertekisliklarning matematik modellari, ularning geometrik tushunchalari va chiziqli qaror qabul qiluvchi qoidalarining asosiy xususiyatlarini tahlil qiladi. Shuningdek, ushbu yondashuvlar turli amaliy sohalarda qanday qo'llanilishi va ularning sun'iy intellekt tizimlaridagi ahamiyati yoritiladi. Maqola, ajratuvchi gipertekisliklar yordamida qaror qabul qilish jarayonining bosqichlari va ularning samaradorligini oshirish yo'llarini ham o'rganadi.
References
James, g., witten, d., hastie, t., & tibshirani, r. (2013). An introduction to statistical learning. Springer.
Alpaydin, e. (2010). Introduction to machine learning. Mit press.
Duda, r. O., hart, p. E., & stork, d. G. (2001). Pattern classification. Wiley-interscience.
Vapnik, v. (1998). Statistical learning theory. Wiley.
Friedman, j., hastie, t., & tibshirani, r. (2001). The elements of statistical learning. Springer.
Mitchell, t. M. (1997). Machine learning. Mcgraw-hill.
Murphy, k. P. (2012). Machine learning: a probabilistic perspective. Mit press.
Boser, b. E., guyon, i. M., & vapnik, v. N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on computational learning theory.
Cortes, c., & vapnik, v. (1995). Support-vector networks. Machine learning.
Scholkopf, b., & smola, a. J. (2002). Learning with kernels. Mit press.
Cristianini, n., & shawe-taylor, j. (2000). An introduction to support vector machines. Cambridge university press.
Ho, t. K. (1995). Random decision forests. Proceedings of the 3rd international conference on document analysis and recognition.
Breiman, l. (2001). Random forests. Machine learning.
Hastie, t., tibshirani, r., & friedman, j. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer.
Tojimamatov, I. N., Olimov, A. F., Khaydarova, O. T., & Tojiboyev, M. M. (2023). CREATING A DATA SCIENCE ROADMAP AND ANALYSIS. PEDAGOGICAL SCIENCES AND TEACHING METHODS, 2(23), 242-250.
Тожимаматов, И. Н. (2023). ЗАДАЧИ ИНТЕЛЛЕКТУАЛЬНОГО АНАЛИЗА ДАННЫХ. PEDAGOG, 6(4), 514-516.
Muqaddam, A., Shahzoda, A., Gulasal, T., & Isroil, T. (2023). NEYRON TARMOQLARDAN FOYDALANIB TASVIRLARNI ANIQLASH USULLARI. SUSTAINABILITY OF EDUCATION, SOCIO-ECONOMIC SCIENCE THEORY, 1(8), 63-74.
Raximov, Q. O., Tojimamatov, I. N., & Xo, H. R. O. G. L. (2023). SUNIY NЕYRON TARMOQLARNI UMUMIY TASNIFI. Scientific progress, 4(5), 99-107.
Ortiqovich, Q. R., & Nurmamatovich, T. I. (2023). NEYRON TARMOQNI O ‘QITISH USULLARI VA ALGORITMLARI. Scientific Impulse, 1(10), 37-46.
Tojimamatov, I. N., Mamalatipov, O., Rahmatjonov, M., & Farhodjonov, S. (2023). NEYRON TARMOQLAR. Наука и инновация, 1(1), 4-12.
Tojimamatov, I. N., Mamalatipov, O. M., & Karimova, N. A. (2022). SUN’IY NEYRON TARMOQLARINI O ‘QITISH USULLARI. Oriental renaissance: Innovative, educational, natural and social sciences, 2(12), 191-203.
Muqaddam, A., Shahzoda, A., Gulasal, T., & Isroil, T. (2023). NEYRON TARMOQLARDAN FOYDALANIB TASVIRLARNI ANIQLASH USULLARI. SUSTAINABILITY OF EDUCATION, SOCIO-ECONOMIC SCIENCE THEORY, 1(8), 63-74.
Raximov, Q. O., Tojimamatov, I. N., & Xo, H. R. O. G. L. (2023). SUNIY NЕYRON TARMOQLARNI UMUMIY TASNIFI. Scientific progress, 4(5), 99-107.
Raxmatjonova, M. N., & Tojimamatov, I. N. (2023). BIZNESDA SUNIY INTELEKT TEXNOLOGYALARI VA ULARNI AHAMIYATI. Лучшие интеллектуальные исследования, 11(3), 46-52.
Nurmatovich, T. I. (2024). Bir qatlamli va ko ‘p qatlamli neyron to ‘rlari. ILM FAN XABARNOMASI, 1(1), 190-191.
Nurmamatovich, T. I., & Kudratullo o‘g, K. U. B. (2024). THE EVOLUTION OF AI: FROM EARLY CONCEPTS TO MODERN BREAKTHROUGHS. Лучшие интеллектуальные исследования, 20(2), 42-46.
Tojimamatov, I., & G’ulomjonova, S. (2024). NEYRO KOMPYUTERLAR VA ULARNING ARXITEKTURASI. Development of pedagogical technologies in modern sciences, 3(6), 10-16.
Tojimamatov, I., & Jo’rayeva, M. (2024). BOLSMAN MASHINASI VA UNING AHAMIYATI. Development and innovations in science, 3(4), 154-160.
Nurmamatovich, T. I., & Nozimaxon, E. (2024). Chiqish qatlami vaznlarni sozlash va xatoliklarni teskari tarqalishi algoritmi. ILM FAN XABARNOMASI, 1(1), 29-35.
Tojimamatov, I., & Ismoiljonova, O. (2024). BIR QATLAMLI PERCEPTRONNI O ‘QITISH. Академические исследования в современной науке, 3(12), 153-158.
Nurmamatovich, T. I. (2024, April). BIR QATLAMLI PERCEPTRONNI O ‘QITISH. In " CANADA" INTERNATİONAL CONFERENCE ON DEVELOPMENTS İN EDUCATİON, SCİENCESAND HUMANİTİES (Vol. 17, No. 1).
Nurmamatovich, T. I. (2024, April). SUN'IY NEYRONNING MATEMATIK MODELI HAMDA FAOLLASHTIRISH FUNKTSIYALARI. In " USA" INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE TOPICAL ISSUES OF SCIENCE (Vol. 17, No. 1).
Nurmamatovich, T. I. (2024, April). SUNIY NEYRON TORLARINI ADAPTIV KUCHAYTIRISH USULI. In " USA" INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE TOPICAL ISSUES OF SCIENCE (Vol. 17, No. 1).
Nurmamatovich, T. I. (2024). XEBB O’QITISH QOIDASI. " GERMANY" MODERN SCIENTIFIC RESEARCH: ACHIEVEMENTS, INNOVATIONS AND DEVELOPMENT PROSPECTS, 17(1).
Tojimamatov, I., & G’ulomjonova, S. (2024). NEYRO KOMPYUTERLAR VA ULARNING ARXITEKTURASI. Development of pedagogical technologies in modern sciences, 3(6), 10-16.
