Abstract
In this paper, we propose an improved randomized based feed forward neural networks, known as Total variance minimization based random vector functional link network (Total-Var-RVFL) and intraclass variance minimization based random vector functional link network (Class-Var-RVFL). Total-Var-RVFL exploits the training data dispersion by minimizing the total variance while as Class-Var-RVFL minimizes the intraclass variance of the training data. The proposed classification models are evaluated on 18 datasets (UCI datasets). From the experimental analysis, one can see that the proposed classification models show better generalization performance as compared to the given baseline models.
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Acknowledgement
This work was supported by Science & Engineering Research Board (SERB) under Ramanujan fellowship Grant No. SB/S2/ RJN-001/2016 and Early Career Research Award Grant No. ECR/2017/000053. It is also supported by Council of Scientific & Industrial Research (CSIR), New Delhi, INDIA under Extra Mural Research (EMR) Scheme Grant No. 22(0751)/17/ EMR-II. We gratefully acknowledge the Indian Institute of Technology Indore for providing facilities and support.
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Ganaie, M.A., Tanveer, M., Suganthan, P.N. (2020). Minimum Variance Embedded Random Vector Functional Link Network. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_48
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