A comprehensive review on the variants of support vector machines

Kumar, Bagesh; Vyas, O. P.; Vyas, Rushi

doi:10.1142/s0217984919503032

Cited by 32 publications

(13 citation statements)

References 55 publications

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“…This is an indicator of the fact that SVM works well for such problems where the data-sets are relatively small. SVM is a binary linear classifier that maps feature points in space, creating different categories [ 83 ]. These categories are separated by a gap as wide as possible.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Mujahid

Contreras

Vehı́

2021

Sensors

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(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Mujahid

Contreras

Vehı́

2021

Sensors

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“…SVM is a type of classifier that performs binary classification on samples by supervised learning. [21,22] Its goal is to obtain the hyperplane with maximum margin to separate the samples. Compared with the other classification algorithms, the overall dependence of SVM on training samples is small.…”

Section: Establishment Of Svm Model Based On Principal Componentsmentioning

confidence: 99%

Longitudinal Crack Detection Approach Based on Principal Component Analysis and Support Vector Machine for Slab Continuous Casting

Huang

Wei

et al. 2021

steel research int.

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Longitudinal cracks are the typical surface defects of continuous casting slabs, resulting in additional processing or even casting interruption. Monitoring longitudinal crack defects is of great significance in stabilizing and improving the slab quality. Herein, a monitoring model is developed to recognize the longitudinal crack defect of continuous casting slabs using principal component analysis (PCA) and support vector machine (SVM). First, the typical characteristics of the temperature patterns corresponding to the longitudinal crack defect are extracted, including the normal casting temperature with small and large fluctuations, as well as the longitudinal crack temperature. Then, PCA is used to reduce the dimension of these characteristics for removing the redundancy and reducing the computational burden. Subsequently, an SVM is applied to identify normal and longitudinal crack temperature patterns for training the monitoring model PCA–SVM. The monitoring performance is verified by the test data, in which the training accuracy and test accuracy are 100% and 96%, respectively. It is worth mentioning that the model can successfully predict all the real longitudinal crack defects, showing excellent detection performance. The established model is expected to provide a theoretical basis and a reliable way for online monitoring of slab surface cracks.

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“…A hinge loss function for margin maximization along with a regularization parameter makes the SVM a robust classifier. A good review about variants of SVM can be found in [98].…”

Section: ) Support Vector Machinesmentioning

confidence: 99%

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Arif

2021

JISCR

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Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

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A comprehensive review on the variants of support vector machines

Cited by 32 publications

References 55 publications

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Longitudinal Crack Detection Approach Based on Principal Component Analysis and Support Vector Machine for Slab Continuous Casting

A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

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