Introduction of SVM algorithms and recent applications about fault diagnosis and other aspects

Yin, Zuosheng; Liu, Jianxing; Krueger, Minjia; Gao, Huijun

doi:10.1109/indin.2015.7281793

Cited by 13 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on these modifications, it then determines the ideal border between potential outputs. It is often used in a binary classification task [10]. Before derivation, some definitions are given.…”

Section: Support Vector Machinementioning

confidence: 99%

Performance Analysis of Machine Learning Methods

Liang

Xuan

Yuan

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Machine Learning has been studied worldwide for its functions in data science and artificial intelligence (AI) fields. Previous works have shown the excellent performance of machine learning methods in image classification. This paper uses various machine learning methods for fashion product classification. This paper aims to analyze the result of predictions for all classes and the first three ranked classes, and meanwhile, compare and discuss Support Vector Machine (SVM), K Nearest Neighbor (KNN), Convolution Neural Network (CNN), Contrastive Language-Image Pre-training (CLIP) methods’ performance. The results show that the F-Score is increased if just predicted for the first three ranked classes, and among SVM, KNN, and CNN models, CNN is the best in both conditions. From the performance of all four models, CLIP was the best model with better learning ability. Besides, the results suggest that an imbalanced dataset may harm predictions, and the CLIP method yields the best result. In the future, CLIP methods may be more likely recommended in an image classification problem with lots of classes, and an imbalanced dataset adjusted will provide new insights into unsolved and unimproved classification problems.

show abstract

Section: Support Vector Machinementioning

confidence: 99%

Performance Analysis of Machine Learning Methods

Liang

Xuan

Yuan

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Therefore, SVM would be appropriate algorithm for us to build model. [18] Before deriving, some definitions are given. Suppose that a training data set on the feature space is given 𝑇 𝑥 , 𝑦 , 𝑥 , 𝑦 , … , 𝑥 , 𝑦…”

Section: Introduction and The Algorithm Principle Of Svmmentioning

confidence: 99%

COVID-19 infection prediction using physical signs

Wen¹,

Zhang²,

Zhang³

2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

View full text Add to dashboard Cite

Recently, COVID-19 has become a world-wide pandemic with high infection and death rate. Researches mainly focus on how to predict the trend or control the infection. In this paper, we aim to predict COVID-19 pandemic using physical signs. We apply three methods, including decision tree, random forest and supported vector machine. We claim that all the methods achieves a satisfying performance, with the top accuracy is 99.8%. Our method provides a significant contribution in prediction COVID-19 infection, which will apply in real-world application with a real-time inference time.

show abstract

Research on Surface Color Difference of Solar Cells Based on Support Vector Machine

Zhang

Liu

2018

Communications in Computer and Information Science

View full text Add to dashboard Cite

Introduction of SVM algorithms and recent applications about fault diagnosis and other aspects

Cited by 13 publications

References 63 publications

Performance Analysis of Machine Learning Methods

Performance Analysis of Machine Learning Methods

COVID-19 infection prediction using physical signs

Research on Surface Color Difference of Solar Cells Based on Support Vector Machine

Contact Info

Product

Resources

About