Weighted Linear Loss Projection Twin Support Vector Machine for Pattern Classification

Chen, Sugen; Cao, Junfeng; Zhong, Huihuang

doi:10.1109/access.2019.2914022

Cited by 10 publications

(4 citation statements)

References 48 publications

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“…Universities are important bases for cultivating innovative talents, and undergraduate education in universities is a key factor in cultivating innovative talent (Zhang, 2020). To improve the quality of undergraduate education, it is necessary to reform and innovate the training mode of undergraduates (Chen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method of Higher Education Teaching Reform Based on Deep Learning Analysis Technology

Zhang,

Jia,

Angeli

2024

International Journal of Web-Based Learning and Teaching Technologies

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Considering the shortcomings of large evaluation errors, long time, human, and material resources in the evaluation process of the current college teaching mode to improve the accuracy of the evaluation of college teaching mode and reduce the cost of the evaluation, this study proposes an evaluation method for college teaching methods based on deep learning algorithms. Firstly, the research status of the evaluation of college teaching mode is analyzed, and the reasons for the poor evaluation results of the current college teaching mode are found; then the existing deep learning algorithm is improved, and the effectiveness and speed of the method are verified by comparing with other models. Then, the evaluation model of college teaching mode is established, and machine learning is performed on the evaluation data of college teaching mode; finally, the evaluation data of college teaching mode is collected, and the application example test of college teaching mode evaluation is performed.

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Section: Introductionmentioning

confidence: 99%

Evaluation Method of Higher Education Teaching Reform Based on Deep Learning Analysis Technology

Zhang,

Jia,

Angeli

2024

International Journal of Web-Based Learning and Teaching Technologies

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“…Whether it is a regression task or a classification task, it can be achieved by the support vector machine (SVM) method. is study intends to use the SVM algorithm to evaluate the quality of the observation data [12,13]. e SVM algorithm has obvious advantages in solving small sample data and high-dimensional data, and it can map the relationship between input and output data well.…”

Section: Introductionmentioning

confidence: 99%

Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

Tao

2022

Mathematical Problems in Engineering

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The loss function of the traditional support vector machine (SVM) method consists of hinge function and regularization, which is difficult to achieve the quality control of observation data. It requires a new loss function to measure the quality of the observed data. At this stage, researchers will use data cleaning or data preprocessing to process observational data. The data preprocessing method will normalize the data features, which will make the data process the same interval and a distribution. However, this method is also difficult to guarantee the accuracy of the data. This study uses the SVM method to study the integrity, unity, and accuracy of the observation data. This study also proposes a new loss function, which can obtain the uncertainty distribution of the observed data. The loss function of traditional SVM methods is that the uncertainty distribution of the data cannot be obtained. The results show that the SVM algorithm with the new loss function has better accuracy in processing the observed data. For the maximum prediction error of the observed data, the error with the new loss function is only 2.58%, which is reduced by 0.21% compared to the SVM method with the old loss function. However, the prediction error of the SVM algorithm with the old loss function is 2.78%, and the SVM algorithm with the new loss function has obvious prediction accuracy.

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“…Several models based on machine learning have been developed for prediction. The multilayer perceptron (MLP), (11)(12)(13) convolutional neural network (CNN), (14)(15)(16) long short-term memory (LSTM), (17)(18)(19)(20) SVM, (21)(22)(23)(24) and random forest (RF) (25)(26)(27)(28) are among the most popular ones. We analyzed and compared these techniques to find the best fitting model for air temperature and soil moisture forecasts.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor-based Environmental Forecasting System for Smart Durian Farms in Tropical Regions

Kuo¹,

Liou²,

Nilaphruek³

et al. 2021

Sensors and Materials

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Durians are among the most important fruit products in tropical countries. The environments of durians therefore must support a high yield to meet demand. Sunlight, temperature, and rainfall are all key variables, and any adverse factors will have a negative impact on production. We propose an environmental prediction system for a durian farm on the basis of the concept of the IoT. The system uses multiple machine learning algorithms to analyze collected environmental data and predict the next state of the environmental variables. From numerous experiments, our results show that the support vector machine (SVM) gives the best forecasts for temperature, whereas the convolutional neural network (CNN) performs best for predicting soil humidity. The results of this paper can provide farmers with real-time understanding of their farms and early warning of potential risks. The farm yield rates can hence be increased.

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Weighted Linear Loss Projection Twin Support Vector Machine for Pattern Classification

Cited by 10 publications

References 48 publications

Evaluation Method of Higher Education Teaching Reform Based on Deep Learning Analysis Technology

Evaluation Method of Higher Education Teaching Reform Based on Deep Learning Analysis Technology

Application of a New Loss Function-Based Support Vector Machine Algorithm in Quality Control of Measurement Observation Data

Multi-sensor-based Environmental Forecasting System for Smart Durian Farms in Tropical Regions

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