A Sequential Minimal Optimization Algorithm for the All-Distances Support Vector Machine

Candel, Diego C.; Ñanculef, Ricardo; Concha, Carlos; Allende, Héctor

doi:10.1007/978-3-642-16687-7_64

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…The results demonstrate that RVFL correctly classifies the different intrusion signals. El-Said et al [86] conducted experiments with four machine learning algorithms, i.e., support vector machine (SVM) [87], K-nearest neighbor (K-NN) [88], sequential minimal optimization regression [89,90] and RVFL model, to predict the air injection effect on the thermohydraulic performance of shell and tube heat exchanger. The experimental analysis reveals that the RVFL model outperforms compared models with excellent accuracy and better generalization performance.…”

Section: Rvfl With Bayesian Inference (Bi) and Other Techniquesmentioning

confidence: 99%

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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Neural networks have been successfully employed in various domain such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. RVFL model has several characteristics such as fast training speed, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the comprehensive review of the development of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and ensemble deep RVFL models. The variations, improvements and applications of RVFL models are discussed in detail. Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. Finally, we give potential future research directions/opportunities that can inspire the researchers to improve the RVFL architecture further.

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Section: Rvfl With Bayesian Inference (Bi) and Other Techniquesmentioning

confidence: 99%

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…When more than two classes of PQ disturbances are required to be classified, OAA method seems to be appropriate [34]. In the classification task, PQ disturbances as pattern are aimed to be classified into one of all classes.…”

Section: Classification Based On Svmmentioning

confidence: 99%