A Random Vector Functional Link Network Based Content Based Image Retrieval

Mary, S. Sahaya Sujithra; Sasithradevi, A.; Roomi, S. Mohamed Mansoor; Immanuvel, J. Jebas

doi:10.1109/iconstem.2019.8918817

Cited by 1 publication

(1 citation statement)

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“…Incorporating the informative SED, the multi-scale relationship with oil price is explored, and four machine learning models, i.e., ELM, RVFL, linear regression (LR), and backpropagation neural network (BPNN), are employed in this task. Mary et al [96] employed standard RVFL in the image retrival (IR) framework for better performance. To address the instability issue in the sliding mode control system, Zhou and Wu [97] proposed an adaptive fuzzy RVFL (FRVFL), wherein self-mapping between fuzzy rules and hidden layers is employed and adaptive rules are also employed to achieve self-adjustment for the output weights.…”

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%