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Regression depth and support vector machine [PDF]
, 2006 The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable. Christmann and Rousseeuw [CR01] showed that RDM is also useful for the case of binary regression.
Christmann, Andreas
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Ultrasonic Sensor Modeling with Support Vector Regression [PDF]
SensorsThis study proposes a novel approach for predicting the output behaviors of the Pepperl+Fuchs 3RG6232-3JS00-PF ultrasonic sensor. The sensor, integrated into the Festo MPS-PA Didactic System, serves to monitor the water level in a tank, facilitating ...
Duy Ngoc Dang +4 more
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Significance Support Vector Regression for Image Denoising [PDF]
Entropy, 2021 As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the ...
Bing Sun, Xiaofeng Liu
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Kernel-Free Quadratic Surface Support Vector Regression with Non-Negative Constraints [PDF]
Entropy, 2023 In this paper, a kernel-free quadratic surface support vector regression with non-negative constraints (NQSSVR) is proposed for the regression problem. The task of the NQSSVR is to find a quadratic function as a regression function.
Dong Wei +3 more
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Novel Robust Twin Support Vector Regression [PDF]
Jisuanji kexue yu tansuo, 2023 Regression problem is one of the basic problems in the field of pattern recognition and machine learning. Twin support vector regression (TSVR) is a new algorithm to deal with regression problems developed on the basis of support vector regression (SVR).
CHEN Sugen, SHI Ting
doaj +1 more source
Support Vector Regression [PDF]
Proceedings of the Fourth Workshop on Hardware and Architectural Support for Security and Privacy, 2015 Rooted in statistical learning or Vapnik-Chervonenkis (VC) theory, support vector machines (SVMs) are well positioned to generalize on yet-to-be-seen data. The SVM concepts presented in Chapter 3 can be generalized to become applicable to regression problems.
Mariette Awad, Rahul Khanna
openaire +2 more sources
Wavelet Support Vector Censored Regression
Analytics, 2023 Learning methods in survival analysis have the ability to handle censored observations. The Cox model is a predictive prevalent statistical technique for survival analysis, but its use rests on the strong assumption of hazard proportionality, which can ...
Mateus Maia +3 more
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Weighted Smooth Projection Twin Support Vector Regression Algorithm [PDF]
Jisuanji gongcheng, 2022 The existing Pair-shifted Projection Twin Support Vector Regression(PPTSVR) algorithm ignores the effects of samples at different locations on the hyperplane construction during the training process.If there are outliers in the samples, the fitting ...
XU Benye, GU Binjie, PAN Feng, XIONG Weili
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Quantum Support Vector Regression for Disability Insurance
Risks, 2021 We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem.
Boualem Djehiche, Björn Löfdahl
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Estimasi Konsentrasi PM10 Menggunakan Support Vector Regression
JRST: Jurnal Riset Sains dan Teknologi, 2022 PM10 berkontribusi terhadap polusi udara pada saat kejadian kabut asap di musim kemarau dengan salah satu sumber utamanya adalah pembakaran biomassa. Pada saat musim kemarau, terdapat banyak kegiatan pembersihan lahan di Mempawah untuk persiapan masa ...
Zia Ayu Frakusya +2 more
doaj +1 more source