A safe screening based framework for support vector regression

Pan, Xianli; Pang, Xinying; Wang, Hongmei; Xu, Yitian

doi:10.1016/j.neucom.2018.01.083

Cited by 23 publications

(3 citation statements)

References 23 publications

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“…Compared with artificial neural networks, SVM still has a strong generalization ability and global optimization ability when the training data are insufficient. In recent years, it has become a hotspot in various research and industrial process applications and has been successfully applied to modeling and regression analysis problems [130][131][132][133].…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

An Overview of Technological Parameter Optimization in the Case of Laser Cladding

et al. 2023

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This review examines the methods used to optimize the process parameters of laser cladding, including traditional optimization algorithms such as single-factor, regression analysis, response surface, and Taguchi, as well as intelligent system optimization algorithms such as neural network models, genetic algorithms, support vector machines, the new non-dominance ranking genetic algorithm II, and particle swarm algorithms. The advantages and disadvantages of various laser cladding process optimization methods are analyzed and summarized. Finally, the development trend of optimization methods in the field of laser cladding is summarized and predicted. It is believed that the result would serve as a foundation for future studies on the preparation of high-quality laser cladding coatings.

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Section: Support Vector Machines (Svm)mentioning

confidence: 99%

An Overview of Technological Parameter Optimization in the Case of Laser Cladding

et al. 2023

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“…However, the support vectors are determined by the regressor, and it is difficult to obtain before training the model. Meanwhile, the reduction algorithms usually requires to read the entire training data several times, so that they needs lots of time to get the final result [7][8][9][10]. Decomposition algorithms train the SVR model with all the training instances rather than some of them.…”

Section: Introductionmentioning

confidence: 99%

The Mechanism Analysis of the Accelerator for Support Vector Regression Based on Data Partition

Song¹,

Li²,

Liu³

et al. 2020

Fuzzy Systems and Data Mining VI

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Support vector regression is an important algorithm in machine learning, and it is widely used in real life for its good performance, such as house price forecast, disease prediction, weather forecast, and so on. However, it cannot efficiently process large-scale data, because it has a high time complexity in the training process. Data partition as an important solution to solve the large-scale learning problem mainly focuses on the classification task, it trains the classifiers over the divided subsets produced by data partition and obtain the final classifier by combining those classifiers. Meanwhile, the most existing method rarely study the influence of data partition on the regressor performance, so that it is difficult to keep its generation ability. To solve this problem, we obtain the estimation of the difference in objective function before and after the data partition. Mini-Batch K-Means clustering is adopted to largely reduce this difference, and an improved algorithm is proposed. This proposed algorithm includes training stage and prediction stage. In training stag, it uses Mini-Batch K-Means clustering to divide the input space into some disjoint sub-regions of equal sample size, then it trains the regressor on each divided sub-region using support vector regression algorithm. In the prediction stage, the regressor merely offers the predicted label for the unlabeled instances that are in the same sub-region. Experiment results on real datasets illustrate that the proposed algorithm obtains the similar generation ability as the original algorithm, but it has less execution time than other acceleration algorithms.

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“…have been proposed to solve data regression and estimation problems, i.e., the Recurrent Neural Network [1], Time Delay Neural Network [1], Echo State Networks [1], Dual Coordinate Descent Method [2], Successive Overrelaxation Algorithm [2], Modified Newton Method [2], Support Vector Regression [2], Genetic Adaptive Neural Network [3], and Backpropagation Neural Network [3].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network

et al. 2019

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The Generalized Adaptive Resonance Theory (GART) model is a supervised online learning neural network based on an integration of Adaptive Resonance Theory (ART) and the Generalized Regression Neural Network (GRNN). It is capable of online learning, and is suitable for undertaking both classification and regression problems. In this paper, we further enhance GART (EGART) with four improvements to formulate a new EGART model. Three operating strategies for the EGART model to undertake regression problems are suggested. The first operating strategy is a fully online learning EGART model. The second operating strategy involves Ordering Algorithm for determining the presentation sequence of training samples during the initial training of EGART model. This strategy is considered as offline learning because a set of data samples must be available for the Ordering Algorithm to compute the best presentation sequence (hereinafter denoted as Ordered-EGART). The third operating strategy aims to demonstrate online learning capability of EART model (the first operating strategy) can still be resumed after training on the Ordered-EGART. It is most suitable for applications with a set of ready data samples and their sequences are predetermined by Ordering Algorithm prior to training of EGART model in offline mode, and triggers online learning when more new data samples become available (hereinafter denoted as IO-EGART). A series of experiments with five benchmark data sets from various application domains is conducted to assess and compare the effectiveness of the EGART model and three operating strategies with those of other methods published in literature as well as two fire safety engineering problems, i.e., predicting the thermal interface height in a single compartment fire and evacuation times in the event of fire. The results and comparisons with other approaches positively demonstrate the efficacy and applicability of EGART model as a useful data regression model for tackling fire safety engineering problems.INDEX TERMS Adaptive resonance theory, fire evacuation time, general regression neural network, ordering algorithm, regression, thermal interface height.

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A safe screening based framework for support vector regression

Cited by 23 publications

References 23 publications

An Overview of Technological Parameter Optimization in the Case of Laser Cladding

An Overview of Technological Parameter Optimization in the Case of Laser Cladding

The Mechanism Analysis of the Accelerator for Support Vector Regression Based on Data Partition

Hybrid Data Regression Model Based on the Generalized Adaptive Resonance Theory Neural Network

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