OPTImization for MAchine Learning (OPTIMAL) Research Group
INDIAN INSTITUTE OF TECHNOLOGY INDORE
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OPTImization for MAchine Learning (OPTIMAL)
Research Group
Please see GitHub respository for codes.
BOOKS/BOOK CHAPTERS

Book Chapters

  • M.A. Ganaie, M. Tanveer (2022). Energy-based least squares projection twin SVM, Advanced Machine Intelligence and Signal Processing, Lecture Notes in Electrical Engineering 858, Springer.
  • M.A. Ganaie, M. Tanveer (2022). EEG signal classification using robust energy based least squares projection twin support vector machines, IET Book on Medical Information Processing and Security: Techniques and Applications.
  • M.A. Ganaie, M. Tanveer (2020). Robust general twin support vector machines with pinball loss, Springer Book on Machine Learning for Intelligent Multimedia Analytics: Techniques and Applications.
  • B. Richhariya, M. Tanveer (2019) A fuzzy universum support vector machine based on information entropy. In: Tanveer M., Pachori R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore
  • M. Dalal, M. Tanveer, R.B. Pachori (2019) Automated identification system for focal EEG signals using fractal dimension of FAWT-based sub-bands signals. In: Tanveer M., Pachori R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore.

Books

  • DS Sisodia, L. Garg, RB Pachori, M. Tanveer (2022). Machine Intelligence Techniques for Data Analysis and Signal Processing - Proceedings of the 4th International Conference MISP 2022, Lecture Notes in Electrical Engineering, Springer.
  • D. Gupta, S. Banerjee, R.S. Goswami, M. Tanveer and R.B. Pachori (Eds.). (2021). Pattern Recognition and Data Analysis with Applications, Lecture Notes in Electrical Engineering, Springer, https://link.springer.com/book/9789811915192.
  • M. Tanveer and R.B. Pachori (Eds.). (2019). Machine Intelligence and Signal Analysis (Vol. 748). Springer, https://link.springer.com/book/10.1007/978-981-13-0923-6