Tutorial on Support Vector Machines

Bridgelall, Raj

doi:10.20944/preprints202201.0232.v1

Cited by 7 publications

(9 citation statements)

References 5 publications

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“…According to [24], the support vector machine (SVM) has a solution that the maximum margin classifier or hyper-plane concept can overcome the prediction classification problems that occur in other linear classifiers. In classifying the correct prediction of the SVM method, the following mathematical calculations are known.…”

Section: E Support Vector Machinementioning

confidence: 99%

The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine

Ajhari

2023

Appl. Inf. Syst. Manage.

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Online movie streaming platforms have changed the current pattern of watching movies. Besides providing convenience in watching anywhere and anytime, this service is provided at a lower cost to moviegoers. The increase in moviegoers on online streaming platforms has resulted in easy-to-find reviews. This review can determine whether they decide to watch the film or not. The moviegoers' reviews can be easily and quickly found for analysis using sentiment analysis to find a film's worthiness. This study used sentiment analysis in classifying Twitter data predictions using the Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM). In the sentiment analysis of labeling with positive and negative categories, a distilled version of BERT (DistilBERT) was used in this study. With a little human assistance in preprocessing, the model worked objectively with an overall accuracy performance on the confusion matrix of 64.50% for the Multinomial Naive Bayes model and 64.12% for the Support Vector Machine model. Performance evaluation was also carried out by calculating the cross-validation accuracy, which resulted in an accuracy of 72.38% for the MNB. Meanwhile, the SVM model obtained an accuracy of 70.19%.

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Section: E Support Vector Machinementioning

confidence: 99%

The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine

Ajhari

2023

Appl. Inf. Syst. Manage.

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“…The concept is to separate data across a decision boundary (hyperplanes) determined by a small subset of the data (feature vectors). The data subset that supports the decision boundary is called the support vector ( 38 ). The SVM assumes that the multi-feature data are linearly separable in the input space.…”

Section: Model Developmentmentioning

confidence: 99%

“…This analysis uses the radial kernel. Bridgelall ( 37 ), Mountrakis et al ( 38 ), and Yoonsuh and Hu ( 40 ) explain the use of the kernel trick in more detail.…”

Section: Model Developmentmentioning

confidence: 99%

“…A ''kernel trick'' overcomes the problem of the linearity restriction on the decision boundary. The kernel trick uses a transformation function to map the input vector into a higher dimension space by introducing new parameters (38). The ''trick'' part is that the SVM operates only on the vectors in their ambient space, without actually transforming the vector into a higher dimension.…”

Section: Support Vector Machinementioning

confidence: 99%

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Ranking Risk Factors in Financial Losses From Railroad Incidents: A Machine Learning Approach

Dhingra

Bridgelall

et al. 2022

Transportation Research Record: Journal of the Transportation R

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The reported financial losses from railroad accidents since 2009 have been more than US$4.11 billion dollars. This considerable loss is a major concern for the industry, society, and the government. Therefore, identifying and ranking the factors that contribute to financial losses from railroad accidents would inform strategies to minimize them. To achieve that goal, this paper evaluates and compares the results of applying different non-parametric statistical and regression methods to 15 years of railroad Class I freight train accident data. The models compared are random forest, k-nearest neighbors, support vector machines, stochastic gradient boosting, extreme gradient boosting, and stepwise linear regression. The results indicate that these methods are all suitable for analyzing non-linear and heterogeneous railroad incident data. However, the extreme gradient boosting method provided the best performance. Therefore, the analysis used that model to identify and rank factors that contribute to financial losses, based on the gain percentage of the prediction accuracy. The number of derailed freight cars and the absence of territory signalization dominated as contributing factors in more than 57% and 20% of the accidents, respectively. Partial-dependence plots further explore the complex non-linear dependencies of each factor to better visualize and interpret the results.

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“…The SVM then uses that hyperplane to predict the class of a new data object once presented with its feature vector. See Figure 4 [26].…”

Section: Model Buildingmentioning

confidence: 99%

EEG authentication system using fuzzy vault scheme

Albermany

Baqer

2021

Journal of Discrete Mathematical Sciences and Cryptography

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Authentication is the process of recognizing a user's identity by determining claimed user identity by checking user-provided evidence, combining cryptographic with biometric can solve many of security issues, including authentication. Our goal is to try to combine cryptography and biometrics to achieve authentication using fuzzy vault scheme. Electroencephalography (EEG) signals will be used as they are unique and also difficult to expose and copy; also they are difficult to be hack, using nine healthy persons' EEGs from the BCI Competition and extracting power features from signals spectrum of beta and alpha band of EEG signal, the extracted features are from three channels (C3, Cz, and C4), then support vector Machine (SVM) is used for classification. In this chapter, two tasks (left hand and right hand) are used from a four tasks in the dataset, and the system achieves 96.98% validation accuracy, using 10-fold cross-validation on the training set and the model is saved, after extract features, these features will used to be evaluated on a polynomial generated from the secret key using reed Solomon code and chaff points generated using tent map are added to hide the data, which create the final result that is the vault, for decoding the system using Lagrange interpolation for polynomial reconstruction and returning the key.

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Tutorial on Support Vector Machines

Cited by 7 publications

References 5 publications

The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine

The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine

Ranking Risk Factors in Financial Losses From Railroad Incidents: A Machine Learning Approach

EEG authentication system using fuzzy vault scheme

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