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ORCIDReshma Rastogi
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- affiliation: South Asian University, India
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2020 – today
- 2024
[j50]M. Tanveer
, Taniya Rajani, Reshma Rastogi, Yuan-Hai Shao, M. A. Ganaie:
Comprehensive review on twin support vector machines. Ann. Oper. Res. 339(3): 1223-1268 (2024)- [j49]Sambhav Jain, Reshma Rastogi:
Parametric non-parallel support vector machines for pattern classification. Mach. Learn. 113(4): 1567-1594 (2024) - 2023
- [j48]Sanjay Kumar, Nadira Ahmadi, Reshma Rastogi:
Multi-label learning with missing labels using sparse global structure for label-specific features. Appl. Intell. 53(15): 18155-18170 (2023) - [j47]Pritam Anand, Amisha Bharti, Reshma Rastogi:
Time efficient variants of Twin Extreme Learning Machine. Intell. Syst. Appl. 17: 200169 (2023) - [j46]Reshma Rastogi, Sanjay Kumar:
Discriminatory Label-specific Weights for Multi-label Learning with Missing Labels. Neural Process. Lett. 55(2): 1397-1431 (2023) - 2022
- [j45]Reshma Rastogi, Sambhav Jain:
Multi-label learning via minimax probability machine. Int. J. Approx. Reason. 145: 1-17 (2022) - [j44]Reshma Rastogi, Sayed Mortaza:
Imbalance multi-label data learning with label specific features. Neurocomputing 513: 395-408 (2022) - [j43]Sanjay Kumar, Reshma Rastogi:
Low rank label subspace transformation for multi-label learning with missing labels. Inf. Sci. 596: 53-72 (2022) - [c18]Sanjay Kumar, Reshma Rastogi:
Auxiliary Label Embedding for Multi-label Learning with Missing Labels. CVMI 2022: 525-537 - 2021
- [j42]Mohammad Tanveer, Sweta Sharma, Reshma Rastogi, Pritam Anand:
Sparse support vector machine with pinball loss. Trans. Emerg. Telecommun. Technol. 32(2) (2021) - [j41]Reshma Rastogi, Sayed Mortaza:
Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning. Knowl. Based Syst. 229: 107336 (2021) - [j40]Reshma Rastogi, Sweta Sharma:
Ternary tree-based structural twin support tensor machine for clustering. Pattern Anal. Appl. 24(1): 61-74 (2021) - [j39]Sweta Sharma, Reshma Rastogi, Suresh Chandra:
Large-Scale Twin Parametric Support Vector Machine Using Pinball Loss Function. IEEE Trans. Syst. Man Cybern. Syst. 51(2): 987-1003 (2021) - [c17]Reshma Rastogi, Sweta Sharma, Suresh Chandra:
Stochastic Quasi-Newton Minimum Complexity Machine for Pattern Classification. PReMI 2021: 520-528 - [i7]Mohammad Tanveer, Taniya Rajani, Reshma Rastogi, Yuan-Hai Shao:
Comprehensive Review On Twin Support Vector Machines. CoRR abs/2105.00336 (2021) - [i6]Pritam Anand, Reshma Rastogi, Suresh Chandra:
Improvement over Pinball Loss Support Vector Machine. CoRR abs/2106.01109 (2021) - [i5]Reshma Rastogi, Aman Pal:
Efficient Learning of Pinball TWSVM using Privileged Information and its applications. CoRR abs/2107.06744 (2021) - 2020
- [j38]Pritam Anand, Reshma Rastogi, Suresh Chandra:
A class of new Support Vector Regression models. Appl. Soft Comput. 94: 106446 (2020) - [j37]Pritam Anand, Reshma Rastogi, Suresh Chandra:
A new asymmetric ϵ-insensitive pinball loss function based support vector quantile regression model. Appl. Soft Comput. 94: 106473 (2020) - [j36]Reshma Rastogi, Pritam Anand, Suresh Chandra:
Large-margin Distribution Machine-based regression. Neural Comput. Appl. 32(8): 3633-3648 (2020) - [j35]Pooja Saigal, Reshma Rastogi, Suresh Chandra:
Semi-supervised Weighted Ternary Decision Structure for Multi-category Classification. Neural Process. Lett. 52(2): 1555-1582 (2020) - [i4]Pooja Saigal, Reshma Khemchandani:
Nonparallel Hyperplane Classifiers for Multi-category Classification. CoRR abs/2004.07512 (2020)
2010 – 2019
- 2019
- [j34]Reshma Rastogi, Sweta Sharma:
Fast Laplacian twin support vector machine with active learning for pattern classification. Appl. Soft Comput. 74: 424-439 (2019) - [j33]Pooja Saigal, Suresh Chandra, Reshma Rastogi:
Multi-category ternion support vector machine. Eng. Appl. Artif. Intell. 85: 229-242 (2019) - [j32]Reshma Rastogi, Aman Pal:
Fuzzy semi-supervised weighted linear loss twin support vector clustering. Knowl. Based Syst. 165: 132-148 (2019) - [c16]Reshma Rastogi, Simran Popli, Nima Dorji Moktan, Sweta Sharma:
A Unified Multi-label Relationship Learning. ICCSE 2019: 686-691 - [c15]Reshma Rastogi, Ritesh Gangnani:
Semi-supervised Multi-category Classification with Generative Adversarial Networks. PReMI (1) 2019: 286-294 - [i3]Pritam Anand, Reshma Rastogi, Suresh Chandra:
Support Vector Regression via a Combined Reward Cum Penalty Loss Function. CoRR abs/1904.12331 (2019) - [i2]Pritam Anand, Reshma Rastogi, Suresh Chandra:
A new asymmetric ε-insensitive pinball loss function based support vector quantile regression model. CoRR abs/1908.06923 (2019) - [i1]Pritam Anand, Reshma Rastogi, Suresh Chandra:
A ν-support vector quantile regression model with automatic accuracy control. CoRR abs/1910.09168 (2019) - 2018
- [j31]Reshma Khemchandani, Pooja Saigal, Suresh Chandra:
Angle-based twin support vector machine. Ann. Oper. Res. 269(1-2): 387-417 (2018) - [j30]Reshma Rastogi, Aman Pal, Suresh Chandra:
Generalized Pinball Loss SVMs. Neurocomputing 322: 151-165 (2018) - [j29]Reshma Rastogi, Pooja Saigal, Suresh Chandra:
Angle-based twin parametric-margin support vector machine for pattern classification. Knowl. Based Syst. 139: 64-77 (2018) - [j28]Reshma Khemchandani, Keshav Goyal, Suresh Chandra:
Generalized eigenvalue proximal support vector regressor for the simultaneous learning of a function and its derivatives. Int. J. Mach. Learn. Cybern. 9(12): 2059-2070 (2018) - [j27]Reshma Khemchandani, Aman Pal, Suresh Chandra:
Fuzzy least squares twin support vector clustering. Neural Comput. Appl. 29(2): 553-563 (2018) - [j26]Reshma Rastogi, Sweta Sharma, Suresh Chandra:
Robust Parametric Twin Support Vector Machine for Pattern Classification. Neural Process. Lett. 47(1): 293-323 (2018) - [c14]Sweta Sharma, Reshma Rastogi:
Stochastic Conjugate Gradient Descent Twin Support Vector Machine for Large Scale Pattern Classification. Australasian Conference on Artificial Intelligence 2018: 590-602 - [c13]Ankit Kumar, Reshma Rastogi:
Self-Attention Enhanced Recurrent Neural Networks for Sentence Classification. SSCI 2018: 905-911 - [c12]Aman Pal, Reshma Rastogi:
Learning TWSVM using Privilege Information. SSCI 2018: 1548-1554 - [c11]Reshma Rastogi, Hamid Safdari, Sweta Sharma:
Exploring Data Reduction Techniques for Time Efficient Support Vector Machine Classifiers. SSCI 2018: 2053-2059 - [c10]Pritam Anand, Reshma Rastogi, Amisha Bharti:
A Pinball loss based Extreme Learning Machine for pattern classification. SSCI 2018: 2212-2215 - [c9]Sweta Sharma, Reshma Rastogi:
Insensitive Zone based Pinball Loss Twin Support Vector Machine for Pattern Classification. SSCI 2018: 2238-2245 - [c8]Sweta Sharma, Reshma Rastogi:
Maximum Margin Minimum Variance Twin Support Vector Machine. SSCI 2018: 2246-2253 - 2017
- [b1]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Twin Support Vector Machines - Models, Extensions and Applications. Studies in Computational Intelligence 659, Springer 2017, ISBN 978-3-319-46184-7, pp. 1-206 - [j25]Reshma Rastogi, Pritam Anand, Suresh Chandra:
A ν-twin support vector machine based regression with automatic accuracy control. Appl. Intell. 46(3): 670-683 (2017) - [j24]Reshma Rastogi, Pooja Saigal:
Tree-based localized fuzzy twin support vector clustering with square loss function. Appl. Intell. 47(1): 96-113 (2017) - [j23]Pooja Saigal, Vaibhav Khanna, Reshma Rastogi:
Divide and conquer approach for semi-supervised multi-category classification through localized kernel spectral clustering. Neurocomputing 238: 296-306 (2017) - [j22]Reshma Khemchandani, Aman Pal:
Tree based multi-category Laplacian TWSVM for content based image retrieval. Int. J. Mach. Learn. Cybern. 8(4): 1197-1210 (2017) - [c7]Reshma Rastogi, Sweta Sharma:
Tree-Based Structural Twin Support Tensor Clustering with Square Loss Function. PReMI 2017: 28-34 - 2016
- [j21]Reshma Khemchandani, Avikant Bhardwaj, Suresh Chandra:
Single asset optimal trading strategies with stochastic dominance constraints. Ann. Oper. Res. 243(1-2): 211-228 (2016) - [j20]Reshma Khemchandani, Aman Pal:
Multi-category laplacian least squares twin support vector machine. Appl. Intell. 45(2): 458-474 (2016) - [j19]Reshma Khemchandani, Sweta Sharma:
Robust least squares twin support vector machine for human activity recognition. Appl. Soft Comput. 47: 33-46 (2016) - [j18]Reshma Khemchandani, Keshav Goyal, Suresh Chandra:
TWSVR: Regression via Twin Support Vector Machine. Neural Networks 74: 14-21 (2016) - [j17]Reshma Khemchandani, Pooja Saigal, Suresh Chandra:
Improvements on ν-Twin Support Vector Machine. Neural Networks 79: 97-107 (2016) - [c6]Reshma Khemchandani, Aman Pal:
Weighted Linear Loss Twin Support Vector Clustering. CODS 2016: 18:1-18:2 - [c5]Reshma Khemchandani, Sweta Sharma:
Robust Parametric Twin Support Vector Machine and Its Application in Human Activity Recognition. CVIP (1) 2016: 193-203 - 2015
- [j16]Reshma Khemchandani, Pooja Saigal:
Color image classification and retrieval through ternary decision structure based multi-category TWSVM. Neurocomputing 165: 444-455 (2015) - [c4]Reshma Khemchandani, Keshav Goyal, Suresh Chandra:
Twin Support Vector Machine based Regression. ICAPR 2015: 1-6 - 2013
- [j15]Reshma Khemchandani, Anuj Karpatne, Suresh Chandra:
Twin support vector regression for the simultaneous learning of a function and its derivatives. Int. J. Mach. Learn. Cybern. 4(1): 51-63 (2013) - [j14]Reshma Khemchandani, Anuj Karpatne, Suresh Chandra:
Proximal support tensor machines. Int. J. Mach. Learn. Cybern. 4(6): 703-712 (2013) - [j13]Reshma Khemchandani, Nishil Gupta, Arpit Chaudhary, Suresh Chandra:
Optimal execution with weighted impact functions: a quadratic programming approach. Optim. Lett. 7(3): 575-592 (2013) - 2011
- [j12]Reshma Khemchandani, Anuj Karpatne, Suresh Chandra:
Generalized eigenvalue proximal support vector regressor. Expert Syst. Appl. 38(10): 13136-13142 (2011) - 2010
- [j11]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Learning the optimal kernel for Fisher discriminant analysis via second order cone programming. Eur. J. Oper. Res. 203(3): 692-697 (2010) - [j10]M. Arun Kumar, Reshma Khemchandani, Madan Gopal, Suresh Chandra:
Knowledge based Least Squares Twin support vector machines. Inf. Sci. 180(23): 4606-4618 (2010)
2000 – 2009
- 2009
- [j9]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Knowledge based proximal support vector machines. Eur. J. Oper. Res. 195(3): 914-923 (2009) - [j8]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Regularized least squares fuzzy support vector regression for financial time series forecasting. Expert Syst. Appl. 36(1): 132-138 (2009) - [j7]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Optimal kernel selection in twin support vector machines. Optim. Lett. 3(1): 77-88 (2009) - 2008
- [j6]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives. Inf. Sci. 178(17): 3402-3414 (2008) - [j5]Reshma Khemchandani, Jayadeva, Suresh Chandra:
Linear potential proximal support vector machines for pattern classification. Optim. Methods Softw. 23(4): 491-500 (2008) - 2007
- [j4]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Twin Support Vector Machines for Pattern Classification. IEEE Trans. Pattern Anal. Mach. Intell. 29(5): 905-910 (2007) - [j3]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Fuzzy multi-category proximal support vector classification via generalized eigenvalues. Soft Comput. 11(7): 679-685 (2007) - 2006
- [c3]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Regularized Least Squares Fuzzy Support Vector Regression for Time Series Forecasting. IJCNN 2006: 593-598 - [c2]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Regularized Least Squares Twin SVR for the Simultaneous Learning of a Function and its Derivative. IJCNN 2006: 1192-1197 - 2005
- [j2]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Fuzzy linear proximal support vector machines for multi-category data classification. Neurocomputing 67: 426-435 (2005) - [c1]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Fuzzy Proximal Support Vector Classification Via Generalized Eigenvalues. PReMI 2005: 360-363 - 2004
- [j1]Jayadeva, Reshma Khemchandani, Suresh Chandra:
Fast and robust learning through fuzzy linear proximal support vector machines. Neurocomputing 61: 401-411 (2004)
Coauthor Index
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