ARCHIVES

1. P. Borah and D. Gupta, “Unconstrained convex minimization based implicit Lagrangian twin extreme learning machine for classification
(ULTELMC),” Applied Intelligence, vol. 50, no. 4, pp. 1327–1344, 2020.
2. P. Borah and D. Gupta, “Functional iterative approaches for solving support vector classification problems based on generalized Huber
loss,” Neural Computing and Applications, vol. 32, no. 1, pp. 1135–1139, 2020.3. S. Balasundaram and D. Gupta, “Knowledge-based extreme learning machines,” Neural Computing and Applications, vol. 27, no. 6, pp.
1629–1641, 2016.
4. E. Carrizosa, A. Nogales-Gómez, and D. Romero Morales, “Strongly agree or strongly disagree?: rating features in support vector
machines,” Information Sciences, vol. 329, no. C, pp. 256–273, 2016.
5. G. Taherzadeh, Y. Zhou, A. W.-C. Liew, and Y. Yang, “Sequence-based prediction of protein-carbohydrate binding sites using support
vector machines,” Journal of Chemical Information and Modeling, vol. 56, no. 10, pp. 2115–2122, 2016.
6. M. Tanveer, M. A. Khan, and S.-S. Ho, “Robust energy-based least squares twin support vector machines,” Applied Intelligence, vol.
45, no. 1, pp. 174–186, 2016.
7. W. Gu, W.-P. Chen, and C.-H. Ko, “Two smooth support vector machines for -insensitive regression,” Computational Optimization &
Applications, vol. 70, no. 1, pp. 1–29, 2018.
8. T. Tanino, R. Kawachi, and M. Akao, “Performance evaluation of multiobjective multiclass support vector machines maximizing
geometric margins,” Numerical Algebra Control & Optimization, vol. 1, no. 1, pp. 151–169, 2017.
9. M. Malvoni, M. G. De Giorgi, and P. M. Congedo, “Data on support vector machines (SVM) model to forecast photovoltaic power,”
Data in Brief, vol. 9, no. C, pp. 13–16, 2016.
10. R. Darnag, B. Minaoui, and M. Fakir, “QSAR models for prediction study of HIV protease inhibitors using support vector machines,
neural networks and multiple linear regression,” Arabian Journal of Chemistry, vol. 10, no. S1, pp. S600–S608, 2017.
11. J.-Y. Gotoh and S. Uryasev, “Support vector machines based on convex risk functions and general norms,” Annals of Operations
Research, vol. 249, no. 1-2, pp. 1–28, 2017.
12. T. Singh, F. Di Troia, and C. Aaron Visaggio, “Support vector machines and malware detection,” Journal of Computer Virology &
Hacking Techniques, vol. 41, no. 10, pp. 1–10, 2016.
13. J. Li, Y. Cao, and Y. Wang, “Online learning algorithms for double-weighted least squares twin bounded support vector machines,”
Neural Processing Letters, vol. 45, no. 1, pp. 1–21, 2016.
14. C. Ehrentraut, M. Ekholm, H. Tanushi, J. Tiedemann, and H. Dalianis, “Detecting hospital-acquired infections: a document classification
approach using support vector machines and gradient tree boosting,” Health Informatics Journal, vol. 24, no. 1, pp. 24–42, 2016.
15. X. Zhang, Y. Li, and X. Peng, “Brain wave recognition of word imagination based on support vector machines,” Chinese Journal of
Aerospace Medicine, vol. 14, no. 3, pp. 277–281, 2016.
16. J. Nalepa and M. Kawulok, “Selecting training sets for support vector machines: a review,” Artificial Intelligence Review, vol. 52, no.
2, pp. 857–900, 2019.
17. Gangopadhyay, O. Chatterjee, and S. Chakrabartty, “Extended polynomial growth transforms for design and training of generalized
support vector machines,” IEEE Transactions on Neural Networks & Learning Systems, vol. 29, no. 5, pp. 1–14, 2018.
18. Y. Bai and X. Yan, “Conic relaxations for semi-supervised support vector machines,” Journal of Optimization Theory and Applications,
vol. 169, no. 1, pp. 299–313, 2016.
19. L. Zhang, X. Lu, and C. Lu, “National matriculation test prediction based on support vector machines,” Journal of University of Science
& Technology of China, vol. 47, no. 1, pp. 1–9, 2017.
20. M. Ahmer, A. Shah, S. M. Zafi S. Shah et al., “Using non-linear support vector machines for detection of activities of daily living,”
Indian Journal of Science and Technology, vol. 10, no. 36, pp. 1–8, 2017.
21. K. H. Yoo, Y. D. Koo, H. B. Ju, and M. G. Na, “Identification of LOCA and estimation of its break size by multiconnected support
vector machines,” IEEE Transactions on Nuclear Science, vol. 64, no. 10, p. 1, 2017.
22. Y. Lou, Y. Liu, J. K. Kaakinen, and X. Li, “Using support vector machines to identify literacy skills: evidence from eye move ments,”
Behavior Research Methods, vol. 49, no. 3, pp. 887–895, 2017.
23. U. Mageswari and R. Vinodha, “Engine knock detection based on wavelet packet transform and sparse fuzzy least squares support vector
machines (SFLS-SVM),” IIOAB Journal, vol. 7, no. 11, pp. 194–199, 2016.
24. M. Erdem, F. E. Boran, and D. Akay, “Classification of risks of occupational low back disorders with support vector machines,” Human
Factors and Ergonomics in Manufacturing & Service Industries, vol. 26, no. 5, pp. 550–558, 2016.