The improved learning rate for regularized regression with RKBSs

Liu, Huanxiang; Sheng, Baohuai; Ye, Peixin

doi:10.1007/s13042-016-0496-0

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…In order to make the value range of equation ( 7) as consistent with equation (1), suppose a � −b. In addition, the rapid growth of y � e x is mainly reflected in the range of x ≥ 0, but it is not obvious at x < 0. erefore, in order to ensure that equation (7) can achieve the expected effect, let…”

Section: Methods Improvementmentioning

confidence: 99%

“…e rate is provided in both expected mean and empirical mean. e results show that the uniform convexity influences the learning rate (see Liu et al's study [7]). Aiming at the protection of users' commodity viewing privacy in a commercial website, Wu et al [8] propose to construct a group of dummy requests on a trusted client, then, which are submitted together with a user commodity viewing request to the untrusted server-side, so as to confuse and cover up the user preferences.…”

Section: Introductionmentioning

confidence: 93%

“…At present, the methods of studying students' performance and daily behavior are mainly qualitative theoretical guidance [1][2][3][4] and quantitative data analysis [5][6][7][8][9]. Vahedi et al [1] discussed students' in-class information and communication technology, their motivations, and perceptions, as well as their attitudes toward restriction and integration of information and communication technologies in the classroom.…”

Section: Introductionmentioning

confidence: 99%

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An Analysis of Students’ failing in University Based on Least Square Method and a New $arctan - exp$ Logistic Regression Function

Xianghan

Zhang

2022

Mathematical Problems in Engineering

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By improving the logistic regression function and selecting a step-by-step fitting result using the least square method as the input of the logistic regression model, this paper analyzes the situation of students failing the course. Compared with arctan-exp function and sigmoid function, the former has better robustness and stability and makes the results tend to 0 and 1 and be classified. An improved algorithm based on arctan − exp logistic regression and least square method, which combines the advantages of both functions, is studied. Finally, an implementation algorithm is presented to well meet the function. Besides, through a simulation example, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, and it shows that the nonlinear arctan-exp function, which bases on the least square method, is used as the distribution function to predict the effectiveness of students’ failure. The algorithm has been compared and evaluated, which obtains superior results in terms of both accuracy rate and recall rate of the diagnosis results of the students failing.

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Section: Methods Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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An Analysis of Students’ failing in University Based on Least Square Method and a New $arctan - exp$ Logistic Regression Function

Xianghan

Zhang

2022

Mathematical Problems in Engineering

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“…Optimizers. For VGG16 and ResNet50, RAdam 30 optimizer was used with an initial learning rate of 0.001 and a weight decay of 0.0001. On the other hand, PyramidNet110-270 with ShakeDrop regularization was trained using stochastic gradient decent (SGD), consistent with the approach reported by Yamada et al 7 The learning rate was set to 0.1 initially and scaled down by multiplying 0.1 at the 75th and 150th epochs.…”

Section: Analysis For Jpeg Cifar-10mentioning

confidence: 99%

Data augmentation technique for degraded images without losing the classification ability of clean images

Endo

2024

J. Electron. Imag.

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Classification networks of degraded images need to deal with various strengths of degradation, referred to as degradation levels, in practical applications. However, there has been limited exploration of data augmentation techniques for degraded images with various degradation levels. We propose a data augmentation technique to apply distinct data augmentations to both clean and degraded image domains. Specifically, the proposed method uses random erasing and CutBlur data augmentations for a clean and degraded image, respectively. Experimental results show that the proposed method can effectively train a classification network of degraded images without losing the classification ability of clean images. Furthermore, the results also confirm the proposed method's efficacy across various degradations, multiple network architectures, and several datasets.

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“…It is known that the learning rates of a kernel-based regularization algorithm are influenced by the geometry properties, e.g. the capacity, the covering number, the uniformly convexity ( [4,5,20,40]). Some other parameters with respect to the RKHS also influence the learning rates.…”

mentioning

confidence: 99%

Convergence of online pairwise regression learning with quadratic loss

Wang¹,

Chen²,

Sheng³

2020

Communications on Pure &Amp; Applied Analysis

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Recent investigations on the error analysis of kernel regularized pairwise learning initiate the theoretical research on pairwise reproducing kernel Hilbert spaces (PRKHSs). In the present paper, we provide a method of constructing PRKHSs with classical Jacobi orthogonal polynomials. The performance of the kernel regularized online pairwise regression learning algorithms based on a quadratic loss function is investigated. Applying convex analysis and Rademacher complexity techniques, the bounds for the generalization error are provided explicitly. It is shown that the convergence rate can be greatly improved by adjusting the scale parameters in the loss function.1. Introduction. In recent years, online learning algorithms have been attracting the attentions of a lot of researchers in statistical learning theory(see e.g. [21,58,71,73,74] and references therein). In present paper, we shall give an investigation on the convergence analysis of kernel-based regularized online pairwise learning algorithm associating with the quadratic loss.1.1. Online learning algorithms. Let X be a given compact set in the d-dimensional Euclidean space R d , Y ⊂ R. Let ρ be a fixed but unknown probability distribution on Z = X × Y which yields its marginal distribution ρ x on X and its conditional distribution ρ(• | x) at x ∈ X. Then we denote by {z t = (x t , y t )} T t=1 the sample drawn i.i.d.(independently and identically distribution) according to distribution ρ. The aim of regression learning is to find a predictor f : X → R from a hypothesis space such that f (x) is a "good" approximation of y. Let V (r) : R → R +

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The improved learning rate for regularized regression with RKBSs

Cited by 7 publications

References 22 publications

An Analysis of Students’ failing in University Based on Least Square Method and a New $arctan - exp$ Logistic Regression Function

An Analysis of Students’ failing in University Based on Least Square Method and a New $arctan - exp$ Logistic Regression Function

Data augmentation technique for degraded images without losing the classification ability of clean images

Convergence of online pairwise regression learning with quadratic loss

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The improved learning rate for regularized regression with RKBSs

Cited by 7 publications

References 22 publications

An Analysis of Students’ failing in University Based on Least Square Method and a New arctan − exp Logistic Regression Function

An Analysis of Students’ failing in University Based on Least Square Method and a New arctan − exp Logistic Regression Function

Data augmentation technique for degraded images without losing the classification ability of clean images

Convergence of online pairwise regression learning with quadratic loss

Contact Info

Product

Resources

About

An Analysis of Students’ failing in University Based on Least Square Method and a New $arctan - exp$ Logistic Regression Function

An Analysis of Students’ failing in University Based on Least Square Method and a New $arctan - exp$ Logistic Regression Function