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Narayanan U Edakunni
Narayanan U Edakunni
Verified email at cs.man.ac.uk - Homepage
Title
Cited by
Cited by
Year
Beyond Fano's inequality: Bounds on the optimal F-score, BER, and cost-sensitive risk and their implications
MJ Zhao, N Edakunni, A Pocock, G Brown
The Journal of Machine Learning Research 14 (1), 1033-1090, 2013
822013
Cost-sensitive boosting algorithms: Do we really need them?
N Nikolaou, N Edakunni, M Kull, P Flach, G Brown
Machine Learning 104, 359-384, 2016
762016
Predicting arrival times of vehicles based upon observed schedule adherence
A Tripathi, V Rajan, NU Edakunni
US Patent 9,159,032, 2015
582015
Kernel carpentry for online regression using randomly varying coefficient model
NU Edakunni, S Schaal, S Vijayakumar
152006
Method and system for recommending one or more vehicles for one or more requestors
S Jat, K Mukherjee, NU Edakunni, P Manohar
US Patent 9,978,111, 2018
142018
Methods and systems for analyzing customer care data
G Manjunath, A Sharma, NU Edakunni, D Gupta, M Gupta, S Kunde, ...
US Patent App. 15/064,642, 2017
132017
Boosting as a Product of Experts
NU Edakunni, G Brown, T Kovacs
Uncertainty in Artificial Intelligence, 187-194, 2011
122011
Modeling UCS as a mixture of experts
NU Edakunni, T Kovacs, G Brown, JAR Marshall
Proceedings of the 11th Annual conference on Genetic and Evolutionary …, 2009
122009
Fairxgboost: Fairness-aware classification in xgboost
S Ravichandran, D Khurana, B Venkatesh, NU Edakunni
arXiv preprint arXiv:2009.01442, 2020
92020
Efficient online classification using an ensemble of bayesian linear logistic regressors
NU Edakunni, S Vijayakumar
International Workshop on Multiple Classifier Systems, 102-111, 2009
72009
Method and system to predict a communication channel for communication with a customer service
NU Edakunni, S Galhotra
US Patent App. 15/077,085, 2017
62017
Systems and methods for real-time scheduling in a transportation system based upon a user criteria
NU Edakunni, K Baruah
US Patent 11,127,100, 2021
52021
Online, GA based mixture of experts: a probabilistic model of UCS
NU Edakunni, G Brown, T Kovacs
Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011
42011
Use of gps signals from multiple vehicles for robust vehicle tracking
A Sengupta, NU Edakunni
US Patent App. 15/443,295, 2018
32018
Method and system for real-time prediction of crowdedness in vehicles in transit
A Sengupta, K Baruah, S Sankhya, NU Edakunni
US Patent App. 15/271,249, 2018
32018
Probabilistic Dependency Networks for Prediction and Diagnostics
NU Edakunni, A Raghunathan, A Tripathi, J Handley, F Roulland
Transportation Research Board 94th Annual Meeting, 2015
22015
Bayesian locally weighted online learning
NU Edakunni
The University of Edinburgh, 2010
22010
Accurate and Intuitive Contextual Explanations using Linear Model Trees
A Lahiri, NU Edakunni
arXiv preprint arXiv:2009.05322, 2020
12020
Accuracy exponentiation in UCS and its effect on voting margins
T Kovacs, N Edakunni, G Brown
Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011
12011
Simple is better: Making Decision Trees faster using random sampling
VN Kumar, NU Edakunni
arXiv preprint arXiv:2108.08790, 2021
2021
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