Relevant bibliographies by topics / Regressiya

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Regressiya'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Regressiya"

1

Xamdamov, Rustam Xamdamovich, Aziz Ravshanbek o'g'li Ibrohimov, and Elshod Dilshod o'g'li Haydarov. "LOGISTIK REGRESSIYA ASOSIDA TASNIFLASH MASALALARINI YECHISH." RESEARCH AND EDUCATION 1, no. 9 (2022): 162–71. https://doi.org/10.5281/zenodo.7443501.

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Hozirgi kunda ko‘plab masalalarni yechish obyektlarni toifalarga tasniflash muammosiga chambarchas bog‘liq. Toifalarga tasniflashning ko‘plab usullari mavjud. Ularni axborot xavfsizligining turli muammolarini yechishda qo‘llasa bo‘ladi. Shu usullardan biri logistik regressiya bo‘lib, ushbu maqolada logitik regressiya usuli yordamida obyektlarni sinflashtirish masalalari yechilgan, shuningdek optimallashtirish masalasining yechimi ham ishlab chiqilgan. Ishlab chiqilgan algoritmlar yordamida toifalashtirishning muammosining turli masalalarni hal etish mumkin.
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Muhammademinov, Alijon Azizjon o'g'li. "CHIZIQLI REGRESSIYA: TUSHUNCHA VA AMALIYOT." International Journal of Education, Social Science & Humanities. FARS Publishers 11, no. 3 (2023): 480–81. https://doi.org/10.5281/zenodo.7743609.

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Quyidagi mavzuda chiziqli regressiya haqida gap boradi. Biz uchun ma’lum bo’lgan  to’g’ri chiziq tenglamasini sohalarda qo’llanishi haqida gap boradi.Bundan tashqari chiziqli regressiya va sun’iy intelekt haqida ham alohida to’xtalib o’tish zarur.
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Dilafruz, G'ofutjonovna G'oymatova. "PARAMETRLAR BAHOLARINING XUSUSIYATLARINI REGRESSIYA TENGLAMALARIGA BOG'LASH." EURASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES 3, no. 5 (2023): 46–51. https://doi.org/10.5281/zenodo.7927632.

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Maqolada regressiya tenglamaning parametrlarini baholarining xususiyatlari ko`rib chiqilgan. Bunda bitta omil yordamida modelni jarayonda yoritib berish masalasi tushuntirilgan. Bunday masalalarni xal etishda bir omillik modeldan ko`p omilgacha o`tishni taqozo qiladi. Ushbu omillarning barcha samarasi Y tasodifiy o`zgaruvchi bilan baxolanadi. Omillarni tanlash va bosqichini asosiy shartlari mavjud.
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Xushvaqtov, Jaloliddin Gulmurod o'g'li, and Abbos Shavkat o'g'li Abdusattorov. "SEZAR SHIFRLASH ALGORITMI ORQALI SHIFRLANGAN MA'LUMOTNI REGRESSION TAHLIL YORDAMIDA YECHISH HAMDA NATIJALARNINING XATOLIGINI BAHOLASH." Educational Research in Universal Sciences 3, no. 13 (2024): 293–300. https://doi.org/10.5281/zenodo.14564219.

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<em>Ushbu maqolada Sezar shifrlash algoritmi yordamida kodlangan axborotni chiziqli regressiya tahlili yordamida yechish usullari ko&lsquo;rib chiqilgan. Tadqiqot davomida Sezar shifrlash algoritmining asosiy tamoyillari tushuntirilgan va ushbu shifrlash tizimini yechish uchun regressiya yondashuvi qo&lsquo;llanilgan. Natijalar bo&lsquo;yicha Sezar shifrlash algoritmini regressiya yondashuvi yordamida muvaffaqiyatli yechish mumkinligi ko&lsquo;rsatilgan. Shu bilan birga, natijalarning xatoliklari statistik usullar yordamida tahlil qilingan va yechimning ishonchlilik darajasi baholangan. Mazkur
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Axmedova, Nilufar. "INNOVATSION TRANSFARMATSYA JARAYONLARIDA OZIQ-OVQAT SANOATI MUAMMOLARINING O'RGANILGANLIK DARAJASI." SYNAPSES: INSIGHTS ACROSS THE DISCIPLINES 1, no. 4 (2024): 14–16. https://doi.org/10.5281/zenodo.14040022.

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Ushbu maqolada sanoat korxonalarini rivojlantirish, o`zgarib turuvchi raqobat muhiti va bozor sharoitlarida ekonometrik usullar va modellardan foydalanish yordamida makroiqtisodiy bashorat qilish, tavakkalchilik va noaniqlik sharoitida optimal iqtisodiy qarorlar qabul qilish, keyinchalik, bu qarorlar bajarilishini nazorat qilish masalalarining nazariy va amaliy tomonlarini o`rganishda juft regressiya modellashtirish muhim ahamiyatga ega ekanligi yoritilgan.
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Axmedova, Nilufar. "INNOVATSION TRANSFARMATSYA JARAYONLARIDA OZIQ-OVQAT SANOATINI TADQIQ QILINISH TAHLILI." ACUMEN: INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH 1, no. 4 (2024): 16–19. https://doi.org/10.5281/zenodo.14036371.

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Ushbu maqolada sanoat korxonalarini rivojlantirish, o`zgarib turuvchi raqobat muhiti va bozor sharoitlarida ekonometrik usullar va modellardan foydalanish yordamida makroiqtisodiy bashorat qilish, tavakkalchilik va noaniqlik sharoitida optimal iqtisodiy qarorlar qabul qilish, keyinchalik, bu qarorlar bajarilishini nazorat qilish masalalarining nazariy va amaliy tomonlarini o`rganishda juft regressiya modellashtirish muhim ahamiyatga ega ekanligi yoritilgan.
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Bahodir, Abdullayev. "О'zbеkistоn Rеsрublikasining innоvatsiоn kоʻrsatkichlarini kоrrеlyatsiоn rеgrеssiоn taxlili". Yashil iqtisodiyot va taraqqiyot 2, № 11 (2024): 18–25. https://doi.org/10.5281/zenodo.14533579.

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Maqolada mamlakatning Global Innovatsiyalar indeks (GII) koʻrsatkichlari va omillarining mamlakatinnovatsion rivojlanishiga ta&rsquo;sir etishdagi ahamiyati ochib berilgan. Oʻtkazilgan korrelyatsiya va regressiya tahliliinnovatsiyalar indeksi: infratuzilma, bоzоrning rivоjlanganligi, biznеsning rivоjlanganligi, ijоdiy faоliyat natijalari kabiasosiy oʻzgaruvchilar oʻrtasidagi kuchli aloqalarni ta&rsquo;kidlaydi. Ushbu oʻzgaruvchilar uchun regressiya modellarini ishlabchiqish orqali innovatsiyalar indeksiga qanday ta&rsquo;sir qilishini koʻrsatilgan. Tadqiqotda innovatsiya parametrlarini oʻsishi
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Muhabbatxon, Tojiyeva. "Farg'ona vodiysi viloyatlarida kichik biznes ko'rsatkichlarining o'zgarishi: panel regressiya tahlili." «Yashil iqtisodiyot va taraqqiyot» jurnali 3, no. 1 (2025): 8–13. https://doi.org/10.5281/zenodo.15524938.

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Ushbu tadqiqot Farg&lsquo;ona vodiysining Farg&lsquo;ona, Andijon va Namangan viloyatlarida kichik biznesrivojlanishining asosiy yo&lsquo;nalishlarini o&lsquo;rganishga qaratilgan. Tahlil uchun kichik biznesning yalpi hududiy mahsulotdagi(YHM) ulushi, bandlikdagi, eksportdagi va asosiy kapitalga investitsiyalardagi ulushi kabi indikatorlar tanlandi. Ma&rsquo;lumotlarO&lsquo;zbekiston Respublikasi Davlat statistika qo&lsquo;mitasi va viloyat statistika boshqarmalarining rasmiy ko&lsquo;rsatkichlarigaasoslangan. Tadqiqotda uch bosqichli statistik tahlil yondashuvi qo&lsquo;llanilib, avvalo o&lsq
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Ravshanova, Muhayyo Maxmanazarovna. "Umumta'lim muassasalari sоnining ekonometrik tahlilini pedagogik metodlar yordamida amalga oshirish". Yashil iqtisodiyot va taraqqiyot 1, № 4 (2024): 10–14. https://doi.org/10.5281/zenodo.12802332.

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Mazkur maqolada ko&lsquo;p omilli ekonometrik model tuzishda zarur omillarni tanlay bilish, omillar orasidagibog&lsquo;lanish, regressiya tenglamasini tuzish, ekonometrik modelni baholash va Excel dasturi yordamida model tuzish haqidatushuncha va ma&rsquo;lumotlar taqdim etilgan. Shuningdek, muallif tomonidan natijaviy va ta&rsquo;sir etuvchi omillar tanlab olinganbo&lsquo;lib, umumta&rsquo;lim muasssalari soniga ta&rsquo;sir etuvchi omillar sifatida ahоli jоn bоshiga asоsiy kapitalga о&lsquo;zlashtirilganinvestitsiyalar va umumta&rsquo;lim muassasalarida о&lsquo;quvchilar sоni tanlab olingan
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Rustam, Shukurov. "O'zbekistonda oziq-ovqat sanoati korxonalarida ishlab chiqarish holati tahlili." «Yashil iqtisodiyot va taraqqiyot» jurnali 3, no. 2 (2025): 10–18. https://doi.org/10.5281/zenodo.14932909.

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Ushbu maqolada O&lsquo;zbekiston oziq-ovqat sanoati korxonalarida ishlab chiqarish holati o&lsquo;rganilgan.2022-2024-yillar bo&lsquo;yicha statistik, regression, korrelyatsion va ekspert tahlillar asosida sanoatning rivojlanish dinamikasitahlil qilingan. Shuningdek, maqolada O&lsquo;zbekiston oziq-ovqat sanoati korxonalarida ishlab chiqarish holatini tahlil qilishuchun statistik tahlil, korrelyatsion va regressiya tahlili hamda ekspert baholash usuli kabi turli tadqiqot metodlaridanfoydalanilgan. Maqolada ishlab chiqarish jarayonlarini modernizatsiya qilish, eksport salohiyatini oshirish va o
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Dissertations / Theses on the topic "Regressiya"

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McCubbin, Courtney C. "Regressive Play| An Investigation of Regression in the Analytic Container." Thesis, Pacifica Graduate Institute, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13426903.

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<p> This thesis is a heuristic, hermeneutic investigation into regression using the author's experience as a case study. Regressive play and the desire for deeper regression within the analytic container are explored, guided by the question: What is the experience of following one's impulse to regress to more and more primordial states, and what kind of psychological container is needed to facilitate that deepening both inter- and intrapersonally? The author details a history of regression beginning with Sigmund Freud and continuing to psychoanalyst Michael Balint's <i>basic fault,</i> object
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Jacobs, Mary Christine. "Regression Trees Versus Stepwise Regression." UNF Digital Commons, 1992. http://digitalcommons.unf.edu/etd/145.

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Many methods have been developed to determine the "appropriate" subset of independent variables in a multiple variable problem. Some of the methods are application specific while others have a wide range of uses. This study compares two such methods, Regression Trees and Stepwise Regression. A simulation using a known distribution is used for the comparison. In 699 out of 742 cases the Regression Tree method gave better predictors than the Stepwise Regression procedure.
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Hagos, Tesfamichael Marikos. "Estimation of phases for compliant motion : Auto-regressive HMM, multi-class logistic regression, Learning from Demonstration (LfD), Gradient descent optimization." Thesis, Luleå tekniska universitet, Rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-65613.

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Kapur, Atul. "Role of Stochastic Forcing in ENSO Variability in a Coupled GCM." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_theses/168.

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A systematic procedure is designed to evaluate the role of stochastic forcing (SF) in El Nino ? Southern Oscillation (ENSO) exhibited by coupled ocean-atmosphere general circulation models (CGCMs). The procedure is applied on a 163-year run of a CGCM which has realistic representation of ENSO and SF. The realism of ENSO in the CGCM is determined by statistical comparison with a 29-year global reanalysis product. SF is extracted from both the CGCM and reanalysis as residual atmospheric variability uncoupled to the ocean. Further, the Madden-Julian Oscillation (MJO) and non-MJO components are is
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Schwartz, Amanda Jo. "Adaptive Regression Testing Strategies for Cost-Effective Regression Testing." Diss., North Dakota State University, 2013. https://hdl.handle.net/10365/26926.

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Regression testing is an important but expensive part of the software development life-cycle. Many different techniques have been proposed for reducing the cost of regression testing. To date, much research has been performed comparing regression testing techniques, but very little research has been performed to aid practitioners and researchers in choosing the most cost-effective technique for a particular regression testing session. One recent study investigated this problem and proposed Adaptive Regression Testing (ART) strategies to aid practitioners in choosing the most cost-effective tec
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Williams, Ulyana P. "On Some Ridge Regression Estimators for Logistic Regression Models." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3667.

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The purpose of this research is to investigate the performance of some ridge regression estimators for the logistic regression model in the presence of moderate to high correlation among the explanatory variables. As a performance criterion, we use the mean square error (MSE), the mean absolute percentage error (MAPE), the magnitude of bias, and the percentage of times the ridge regression estimator produces a higher MSE than the maximum likelihood estimator. A Monto Carlo simulation study has been executed to compare the performance of the ridge regression estimators under different experimen
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Wong, Deanna A. "Cytokine profiles in regressing and non-regressing basal cell carcinomas." Thesis, The University of Sydney, 1998. https://hdl.handle.net/2123/27687.

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Sánchez, Lozano Enrique. "Continuous regression : a functional regression approach to facial landmark tracking." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/43300/.

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Facial Landmark Tracking (Face Tracking) is a key step for many Face Analysis systems, such as Face Recognition, Facial Expression Recognition, or Age and Gender Recognition, among others. The goal of Facial Landmark Tracking is to locate a sparse set of points defining a facial shape in a video sequence. These typically include the mouth, the eyes, the contour, or the nose tip. The state of the art method for Face Tracking builds on Cascaded Regression, in which a set of linear regressors are used in a cascaded fashion, each receiving as input the output of the previous one, subsequently redu
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Kazemi, Seyed Mehran. "Relational logistic regression." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50091.

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Aggregation is a technique for representing conditional probability distributions as an analytic function of parents. Logistic regression is a commonly used representation for aggregators in Bayesian belief networks when a child has multiple parents. In this thesis, we consider extending logistic regression to directed relational models, where there are objects and relations among them, and we want to model varying populations and interactions among parents. We first examine the representational problems caused by population variation. We show how these problems arise even in simple cases wit
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Zuo, Yanling. "Monotone regression functions." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29457.

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In some applications, we require a monotone estimate of a regression function. In others, we want to test whether the regression function is monotone. For solving the first problem, Ramsay's, Kelly and Rice's, as well as point-wise monotone regression functions in a spline space are discussed and their properties developed. Three monotone estimates are defined: least-square regression splines, smoothing splines and binomial regression splines. The three estimates depend upon a "smoothing parameter": the number and location of knots in regression splines and the usual [formula omitted] in smo
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Books on the topic "Regressiya"

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian D. Marx. Regression. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-63882-8.

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Fahrmeir, Ludwig, Thomas Kneib, and Stefan Lang. Regression. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01837-4.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9.

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Bingham, N. H., and John M. Fry. Regression. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-969-5.

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Winter, Susan, and Hari Rajagopalan. Regression. SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071908075.

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Hao, Lingxin, and Daniel Naiman. Quantile Regression. SAGE Publications, Inc., 2007. http://dx.doi.org/10.4135/9781412985550.

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Fox, John. Regression Diagnostics. SAGE Publications, Inc., 1991. http://dx.doi.org/10.4135/9781412985604.

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Breen, Richard. Regression Models. SAGE Publications, Inc., 1996. http://dx.doi.org/10.4135/9781412985611.

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Kalisch, Markus, and Lukas Meier. Logistische Regression. Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-34225-8.

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Groß, Jürgen. Linear Regression. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55864-1.

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Book chapters on the topic "Regressiya"

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Introduction." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_1.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Quantile Regression." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_10.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Regression Models." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_2.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "The Classical Linear Model." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_3.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Extensions of the Classical Linear Model." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_4.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Generalized Linear Models." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_5.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Categorical Regression Models." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_6.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Mixed Models." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_7.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Nonparametric Regression." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_8.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Structured Additive Regression." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_9.

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Conference papers on the topic "Regressiya"

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Das, Suddhasvatta. "Agile Regression Testing." In 2024 IEEE Conference on Software Testing, Verification and Validation (ICST). IEEE, 2024. http://dx.doi.org/10.1109/icst60714.2024.00054.

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Primantara, Ari, Udisubakti Ciptomulyono, and Berlian Al Kindhi. "Bagging System Performance Analysis Using Artificial Neural Network, Random Forest Regression, Linear Regression, and Support Vector Regression." In 2024 IEEE International Symposium on Consumer Technology (ISCT). IEEE, 2024. https://doi.org/10.1109/isct62336.2024.10791247.

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Li, Qianru, Christophe Tricaud, Rongtao Sun, and YangQuan Chen. "Great Salt Lake Surface Level Forecasting Using FIGARCH Model." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34909.

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In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we divide the time series in two parts (first 2000 measurement points in Part-1 and the rest is Part-2), we found that for Part-2 data, FIGARCH offers best performance indicating that conditional heteroscedast
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Priya, S. Selva, and Lavanya Gupta. "Predicting the future in time series using auto regressive linear regression modeling." In 2015 Twelfth International Conference on Wireless and Optical Communications Networks (WOCN). IEEE, 2015. http://dx.doi.org/10.1109/wocn.2015.8064521.

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Anguita, Davide, Luca Ghelardoni, and Alessandro Ghio. "Long-term energy load forecasting using Auto-Regressive and approximating Support Vector Regression." In 2012 IEEE International Energy Conference (ENERGYCON 2012). IEEE, 2012. http://dx.doi.org/10.1109/energycon.2012.6348269.

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Johnson, Jack L., Jose Garcia-Bravo, Pawan Panwar, and Paul Michael. "Strategies to Minimize Data Sample Size for Regression-Based Pump/Motor Models." In SICFP’21 The 17:th Scandinavian International Conference on Fluid Power. Linköping University Electronic Press, 2021. http://dx.doi.org/10.3384/ecp182p134.

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This work presents an analysis for tracking the evolution of regression coefficients and the Root-Mean-Square of their residuals on a test dataset for a hydraulic pump. The method starts by iteratively regressing data points that are undergoing sequencing by adding one new data sample at a time, then regressing with each iteration. This process was named Progressively Sequenced Regression Analysis, shortened to “PSR analysis” in this paper. The motivating and guiding postulate of PSR analysis is based on the belief that a plateau of the regression coefficients and statistical figures of merit
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Xia, Haifeng, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Philip Orlik, and Zhengming Ding. "Adversarial Bi-Regressor Network for Domain Adaptive Regression." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/501.

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Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross- domain regression model. Specifically, a discrepant bi-regressor architecture is developed to maximize the difference of bi-regressor to discover uncertain target instances far from the source distribution, and then an
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Jhinaoui, Ahmed, Amanda Pieyre, Areti Malkogianni, Hasani Azamar, and Elias Tsoutsanis. "Attentional neural network for the prediction of gas turbine CO and NOx emission." In GPPS Chania24. GPPS, 2024. http://dx.doi.org/10.33737/gpps24-tc-154.

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This study aims to predict Carbon Monoxide (CO) and Nitrogen Oxide (NOx) emissions from industrial gas turbines during operation, using various measured variables (e.g., turbine inlet temperature, exhaust temperature). This prediction task can be categorized as a multivariate time series regression. Deep learning architectures have been extensively used in the literature to achieve this, primarily based on Long-Short-Term-Memory (LSTM) and Convolutional Neural Networks (CNN), which were originally developed for Natural Language Processing (NLP) and computer vision. While LSTM and CNNs are comm
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Liu, Shuai, Boyang Li, Zhiyu Fang, and Kai Huang. "DCDet: Dynamic Cross-based 3D Object Detector." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/124.

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Recently, significant progress has been made in the research of 3D object detection. However, most prior studies have focused on the utilization of center-based or anchor-based label assignment schemes. Alternative label assignment strategies remain unexplored in 3D object detection. We find that the center-based label assignment often fails to generate sufficient positive samples for training, while the anchor-based label assignment tends to encounter an imbalanced issue when handling objects with different scales. To solve these issues, we introduce a dynamic cross label assignment (DCLA) sc
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Hernandez, Rowell Marquez, John Joseph Castillo, Adrian Angel Ragasa, Celinne Mendez, and Fatima Agdon. "Energy Consumption Forecasting for Smart Industry Using Auto-Regressive Integrated Moving Average (ARIMA) and Vector Auto-Regression (VAR) Model." In SIET 2023: International Conference on Sustainable Information Engineering and Technology. ACM, 2023. http://dx.doi.org/10.1145/3626641.3627607.

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Reports on the topic "Regressiya"

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Stouli, Sami, and Richard Spady. Dual regression. Institute for Fiscal Studies, 2016. http://dx.doi.org/10.1920/wp.cem.2016.0516.

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Stouli, Sami, and Richard Spady. Dual regression. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.01.

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Stouli, Sami, and Richard Spady. Dual regression. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.0119.

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Spady, Richard. Dual Regression. The IFS, 2012. http://dx.doi.org/10.1920/re.ifs.2024.0350.

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Nesheim, Lars. Regression Analysis. The IFS, 2011. http://dx.doi.org/10.1920/ps.ifs.2024.0922.

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Wallstrom, Timothy Clarke, and David Mitchell Higdon. Hierarchical Linear Regression. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1489929.

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Burke, Kevin. Regression in Analysis. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada494895.

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Lockwood, Benjamin, and Dmitry Taubinsky. Regressive Sin Taxes. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23085.

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Boggs, Paul T., and Janet R. Donaldson. Orthogonal distance regression. National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4197.

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Strichman, Ofer. Software Regression Verification. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada594501.

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