Reshma Rastogi

Associate Professor

PhD (Indian Institute of Technology, Delhi, 2008)

Machine Learning and Statistical Inference Click to visit ....Laboratory

Contact Details

reshma.khemchandani(at)sau.ac.in

South Asian University ,Rajpur Rd, Maidan Garhi, New Delhi, Delhi 110068

Profile

Reshma (nee Khemchandani) Rastogi received the Ph.D. degree in machine learning from the Indian Institute of Technology Delhi, New Delhi, India, in 2008. She is currently an Associate Professor with the Department of Computer Science, South Asian University, New Delhi. She has published over 42 papers in refereed international journals and over 16 papers in refereed international conferences in these areas. Her recent work on TWSVM has been cited over 1250 times and has been a subject of review articles in multiple highly reputed journals. She has co-authored two books including Twin Support Vector Machines: Models, Extensions and Applications (Springer) and Financial Mathematics: An Introduction. Her current research interests include machine learning, image processing, financial modeling, and optimization. She has supervised four research students so far in the field of machine learning. Dr. Rastogi has also served on the Steering and Program Committees of several international conferences and on the reviewing boards of many peer reviewed journals including IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Knowledge-Based Systems, and Information Science.

Link of Google Scholar : ‪Reshma Rastogi ( nee Khemchandani)‬ – ‪Google Scholar

Qualifications

PhD in Mathematical Programming Applications in Machine Learning from Indian Institute of Technology Delhi , in 2008

Research Interests

Machine Learning
Optimization
Optimal Trading Strategies
Image Processing

Recent Publications

Sharma, S., Rastogi, R. and Chandra, S, “Large-Scale Twin Parametric Support Vector Machine Using Pinball Loss Function, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, Vol 51 (2), pp. 987 – 1003.
Rastogi, R. and Mortaza, S., “Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning”, Knowledge Based Systems, 2021, 229, 107336.
S Jain, R Rastogi, “Multi-label Minimax Probability Machine with Multi-manifold Regularisation”, Research Reports on Computer Science, 44-63, 2021.
M Tanveer, S Sharma, R Rastogi, P Anand, “Sparse support vector machine with pinball loss”, Transactions on Emerging Telecommunications Technologies 32 (2), e3820, 2021, 1-6.
R Rastogi, S Sharma, “Ternary tree-based structural twin support tensor machine for clustering, Pattern Analysis and Applications, Springer, 2021, 24 (1), 61-74
P Anand, R Rastogi, S Chandra, “A class of new support vector regression models”, Applied Soft Computing, 2020, 94, 106446
P Anand, R Rastogi, S Chandra,“A new asymmetric ϵ-insensitive pinball loss function based support vector quantile regression model”, Applied Soft Computing, 2020, 94, 1064-73
R Rastogi, P Anand, S Chandra, “Large-margin distribution machine-based regression”, Neural Computing and Applications Springer,2020, 32 (8), 3633-3648
P Saigal, R Rastogi, S Chandra, “Semi-supervised Weighted Ternary Decision Structure for Multi-category Classification”, Neural Processing Letters, Springer, 2020, 52 (2), 1555-1582
Reshma Rastogi, Pooja Saigal, and Suresh Chandra. “Angle-based twin parametric-margin support vector machine for pattern classification.” Knowledge-Based Systems 139 (2018): 64-77.
Reshma Khemchandani, Aman Pal, and Suresh Chandra. “Fuzzy least squares twin support vector clustering.” Neural computing and applications 29.2 (2018): 553-563.
Reshma Rastogi, Sweta Sharma, and Suresh Chandra. “Robust parametric twin support vector machine for pattern classification.” Neural Processing Letters 47.1 (2018): 293-323.
Reshma Rastogi, Pritam Anand, and Suresh Chandra. “A ν-twin support vector machine based regression with automatic accuracy control.” Applied Intelligence 46.3 (2017): 670-683.
Reshma Khemchandani, Pooja Saigal, and Suresh Chandra. “Angle-based twin support vector machine.” Annals of Operations Research (2017): 1-31.
Pooja Saigal, Vaibhav Khanna, and Reshma Rastogi. “Divide and conquer approach for semi-supervised multi-category classification through localized kernel spectral clustering.” Neurocomputing 238 (2017): 296-306.
Reshma Khemchandani, Keshav Goyal, and Suresh Chandra. “Generalized eigenvalue proximal support vector regressor for the simultaneous learning of a function and its derivatives.” International Journal of Machine Learning and Cybernetics (2017): 1-12.
Reshma Rastogi, Pritam Anand, and Suresh Chandra. “L1-norm Twin Support Vector Machine-based Regression.” Optimization 66.11 (2017): 1895-1911.
Reshma Khemchandani and Aman Pal. “Tree based multi-category Laplacian TWSVM for content based image retrieval.” International Journal of Machine Learning and Cybernetics 8.4 (2017): 1197-1210.
Reshma Rastogi and Pooja Saigal. “Tree-based localized fuzzy twin support vector clustering with square loss function.” Applied Intelligence 47.1 (2017): 96-113.
Reshma Khemchandani, Pooja Saigal, and Suresh Chandra. “Improvements on ν-twin support vector machine.” Neural Networks 79 (2016): 97-107.
Reshma Khemchandani and Aman Pal. “Multi-category laplacian least squares twin support vector machine.” Applied Intelligence 45.2 (2016): 458-474.
Reshma Khemchandani and Sweta Sharma. “Robust least squares twin support vector machine for human activity recognition.” Applied Soft Computing 47 (2016): 33-46.
Reshma Khemchandani, Avikant Bhardwaj, and Suresh Chandra. “Single asset optimal trading strategies with stochastic dominance constraints.” Annals of Operations Research 243.1-2 (2016): 211-228.
Reshma Khemchandani, Keshav Goyal, and Suresh Chandra. “TWSVR: regression via twin support vector machine.” Neural Networks 74 (2016): 14-21.
Reshma Khemchandani and Pooja Saigal. “Color image classification and retrieval through ternary decision structure based multi-category TWSVM.” Neurocomputing 165 (2015): 444-455.

Last Updated : 20 May 2024