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B. John Oommen
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- affiliation: Carleton University, Ottawa, Canada
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2020 – today
- 2024
- [j191]Rebekka Olsson Omslandseter, Lei Jiao, Xuan Zhang, Anis Yazidi, B. John Oommen:
The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme With Fast Convergence and Epsilon-Optimality. IEEE Trans. Neural Networks Learn. Syst. 35(6): 8278-8292 (2024) - 2023
- [j190]Rebekka Olsson Omslandseter, Lei Jiao, B. John Oommen:
Pioneering approaches for enhancing the speed of hierarchical LA by ordering the actions. Inf. Sci. 647: 119487 (2023) - [j189]Ismail Hassan, B. John Oommen, Anis Yazidi:
Adaptive learning with artificial barriers yielding Nash equilibria in general games. Knowl. Eng. Rev. 38 (2023) - [j188]Rebekka Olsson Omslandseter, Lei Jiao, Yuanwei Liu, B. John Oommen:
User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution. Pattern Anal. Appl. 26(1): 1-17 (2023) - [j187]B. John Oommen, Rebekka Olsson Omslandseter, Lei Jiao:
Learning automata-based partitioning algorithms for stochastic grouping problems with non-equal partition sizes. Pattern Anal. Appl. 26(2): 751-772 (2023) - [j186]B. John Oommen, Rebekka Olsson Omslandseter, Lei Jiao:
The object migration automata: its field, scope, applications, and future research challenges. Pattern Anal. Appl. 26(3): 917-928 (2023) - [j185]Anis Yazidi, Daniel Silvestre, B. John Oommen:
Solving Two-Person Zero-Sum Stochastic Games With Incomplete Information Using Learning Automata With Artificial Barriers. IEEE Trans. Neural Networks Learn. Syst. 34(2): 650-661 (2023) - 2022
- [c199]Rebekka Olsson Omslandseter, Lei Jiao, Xuan Zhang, Anis Yazidi, B. John Oommen:
The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements. AI 2022: 507-518 - [c198]Rebekka Olsson Omslandseter, Lei Jiao, B. John Oommen:
Enhancing the Speed of Hierarchical Learning Automata by Ordering the Actions - A Pioneering Approach. AI 2022: 775-788 - [c197]B. John Oommen, Xuan Zhang, Lei Jiao:
A Comprehensive Survey of Estimator Learning Automata and Their Recent Convergence Results. Honoring Professor Mohammad S. Obaidat 2022: 33-52 - [c196]Ismail Hassan, B. John Oommen, Anis Yazidi:
Learning Automata with Artificial Reflecting Barriers in Games with Limited Information. FLAIRS 2022 - [i2]Ismail Hassan, Anis Yazidi, B. John Oommen:
Adaptive Learning with Artificial Barriers Yielding Nash Equilibria in General Games. CoRR abs/2203.15780 (2022) - 2021
- [j184]Omar Ghaleb, B. John Oommen:
On solving single elevator-like problems using a learning automata-based paradigm. Evol. Syst. 12(1): 37-56 (2021) - [j183]O. Ekaba Bisong, B. John Oommen:
On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists. Evol. Syst. 12(1): 123-154 (2021) - [j182]O. Ekaba Bisong, B. John Oommen:
On utilizing the transitivity pursuit-enhanced object partitioning to optimize self-organizing lists-on-lists. Evol. Syst. 12(3): 655-686 (2021) - [j181]Fatemeh Mahmoudi, Mostafa Razmkhah, B. John Oommen:
Nonparametric "anti-Bayesian" quantile-based pattern classification. Pattern Anal. Appl. 24(1): 75-87 (2021) - [j180]Tahira Ghani, B. John Oommen:
On utilizing 2D features from 3D scans to enhance the prediction of lung cancer survival rates. Pattern Recognit. Lett. 152: 56-62 (2021) - [j179]Anis Yazidi, Ismail Hassan, Hugo Lewi Hammer, B. John Oommen:
Achieving Fair Load Balancing by Invoking a Learning Automata-Based Two-Time-Scale Separation Paradigm. IEEE Trans. Neural Networks Learn. Syst. 32(8): 3444-3457 (2021) - [c195]Rebekka Olsson Omslandseter, Lei Jiao, B. John Oommen:
A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes. IEA/AIE (2) 2021: 227-239 - [c194]Rebekka Olsson Omslandseter, Lei Jiao, B. John Oommen:
Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes. AIAI 2021: 129-142 - 2020
- [j178]Abdolreza Shirvani, B. John Oommen:
On enhancing the deadlock-preventing object migration automaton using the pursuit paradigm. Pattern Anal. Appl. 23(2): 509-526 (2020) - [j177]Xuan Zhang, Lei Jiao, B. John Oommen, Ole-Christoffer Granmo:
A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton. IEEE Trans. Neural Networks Learn. Syst. 31(1): 284-294 (2020) - [j176]Anis Yazidi, Xuan Zhang, Lei Jiao, B. John Oommen:
The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions. IEEE Trans. Neural Networks Learn. Syst. 31(2): 512-526 (2020) - [c193]Tahira Ghani, B. John Oommen:
Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis. Australasian Conference on Artificial Intelligence 2020: 42-54 - [c192]Tahira Ghani, B. John Oommen:
Enhancing the Prediction of Lung Cancer Survival Rates Using 2D Features from 3D Scans. ICIAR (2) 2020: 202-215 - [c191]Rebekka Olsson Omslandseter, Lei Jiao, Yuanwei Liu, B. John Oommen:
User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution. IEA/AIE 2020: 299-311 - [c190]Ibrahim Helmy, B. John Oommen:
A Novel Learning Automata-Based Strategy to Generate Melodies from Chordal Inputs. AIAI (1) 2020: 203-215 - [c189]O. Ekaba Bisong, B. John Oommen:
Optimizing Self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning. AIAI (1) 2020: 227-240
2010 – 2019
- 2019
- [j175]Hanane Tavasoli, B. John Oommen, Anis Yazidi:
On utilizing weak estimators to achieve the online classification of data streams. Eng. Appl. Artif. Intell. 86: 11-31 (2019) - [c188]Nicolas Perez, B. John Oommen:
Multi-Minimax: A New AI Paradigm for Simultaneously-Played Multi-player Games. Australasian Conference on Artificial Intelligence 2019: 41-53 - [c187]Abdolreza Shirvani, B. John Oommen:
The Power of the "Pursuit" Learning Paradigm in the Partitioning of Data. EANN 2019: 3-16 - [c186]Omar Ghaleb, B. John Oommen:
Learning Automata-Based Solutions to the Multi-Elevator Problem. ICIC (3) 2019: 130-141 - [c185]O. Ekaba Bisong, B. John Oommen:
Optimizing Self-organizing Lists-on-Lists Using Pursuit-Oriented Enhanced Object Partitioning. ICIC (3) 2019: 201-212 - [c184]Jessica Havelock, B. John Oommen, Ole-Christoffer Granmo:
On Using "Stochastic Learning on the Line" to Design Novel Distance Estimation Methods for Three-Dimensional Environments. IEA/AIE 2019: 39-49 - [c183]Abdolreza Shirvani, B. John Oommen:
The Power of the "Pursuit" Learning Paradigm in the Partitioning of Data. AIAI 2019: 3-16 - [c182]Omar Ghaleb, B. John Oommen:
Learning Automata-Based Solutions to the Single Elevator Problem. AIAI 2019: 439-450 - [c181]O. Ekaba Bisong, B. John Oommen:
Optimizing Self-organizing Lists-on-Lists Using Enhanced Object Partitioning. AIAI 2019: 451-463 - 2018
- [j174]Rajan Thapa, Lei Jiao, B. John Oommen, Anis Yazidi:
A Learning Automaton-Based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids. IEEE Access 6: 5348-5361 (2018) - [j173]Abdolreza Shirvani, B. John Oommen:
On Invoking Transitivity to Enhance the Pursuit-Oriented Object Migration Automata. IEEE Access 6: 21668-21681 (2018) - [j172]Anis Yazidi, Hugo Hammer, B. John Oommen:
Higher-Fidelity Frugal and Accurate Quantile Estimation Using a Novel Incremental Discretized Paradigm. IEEE Access 6: 24362-24374 (2018) - [j171]Jessica Havelock, B. John Oommen, Ole-Christoffer Granmo:
Novel Distance Estimation Methods Using "Stochastic Learning on the Line" Strategies. IEEE Access 6: 48438-48454 (2018) - [j170]Spencer Polk, B. John Oommen:
Challenging state-of-the-art move ordering with Adaptive Data Structures. Appl. Intell. 48(5): 1128-1147 (2018) - [j169]Spencer Polk, B. John Oommen:
Novel threat-based AI strategies that incorporate adaptive data structures for multi-player board games. Appl. Intell. 48(8): 1893-1911 (2018) - [j168]Ratish Mohan, Anis Yazidi, Boning Feng, B. John Oommen:
On optimizing firewall performance in dynamic networks by invoking a novel swapping window-based paradigm. Int. J. Commun. Syst. 31(15) (2018) - [j167]Akaki Jobava, Anis Yazidi, B. John Oommen, Kyrre M. Begnum:
On achieving intelligent traffic-aware consolidation of virtual machines in a data center using Learning Automata. J. Comput. Sci. 24: 290-312 (2018) - [j166]Abdolreza Shirvani, B. John Oommen:
On enhancing the object migration automaton using the Pursuit paradigm. J. Comput. Sci. 24: 329-342 (2018) - [j165]Hugo Lewi Hammer, Anis Yazidi, B. John Oommen:
On the classification of dynamical data streams using novel "Anti-Bayesian" techniques. Pattern Recognit. 76: 108-124 (2018) - [c180]Jessica Havelock, B. John Oommen, Ole-Christoffer Granmo:
On Using "Stochastic Learning on the Line" to Design Novel Distance Estimation Methods. IEA/AIE 2018: 34-42 - [c179]Armando H. Taucer, Spencer Polk, B. John Oommen:
On Addressing the Challenges of Complex Stochastic Games Using "Representative" Moves. AIAI 2018: 3-13 - [c178]Anis Yazidi, Xuan Zhang, Lei Jiao, B. John Oommen:
The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions. AIAI 2018: 451-461 - 2017
- [j164]Nathan Bell, B. John Oommen:
A novel abstraction for swarm intelligence: particle field optimization. Auton. Agents Multi Agent Syst. 31(2): 362-385 (2017) - [j163]B. John Oommen, Sang-Woon Kim:
Occlusion-based estimation of independent multinomial random variables using occurrence and sequential information. Eng. Appl. Artif. Intell. 63: 69-84 (2017) - [j162]Anis Yazidi, B. John Oommen:
A novel technique for stochastic root-finding: Enhancing the search with adaptive d-ary search. Inf. Sci. 393: 108-129 (2017) - [j161]Hugo Lewi Hammer, Anis Yazidi, B. John Oommen:
"Anti-Bayesian" flat and hierarchical clustering using symmetric quantiloids. Inf. Sci. 418: 495-512 (2017) - [j160]Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo:
The design of absorbing Bayesian pursuit algorithms and the formal analyses of their ε-optimality. Pattern Anal. Appl. 20(3): 797-808 (2017) - [j159]Anis Yazidi, B. John Oommen, Morten Goodwin:
On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments. IEEE Trans. Cybern. 47(7): 1604-1617 (2017) - [c177]Anis Yazidi, Hugo Lewi Hammer, B. John Oommen:
A Higher-Fidelity Frugal Quantile Estimator. ADMA 2017: 76-86 - [c176]Anis Yazidi, B. John Oommen, Morten Goodwin:
Identifying Unreliable Sensors Without a Knowledge of the Ground Truth in Deceptive Environments. ADMA 2017: 741-753 - [c175]Hugo Lewi Hammer, Anis Yazidi, B. John Oommen:
On using novel "Anti-Bayesian" techniques for the classification of dynamical data streams. CEC 2017: 1173-1182 - [c174]Thomas McMahon, B. John Oommen:
Enhancing English-Japanese Translation Using Syntactic Pattern Recognition Methods. CORES 2017: 33-42 - [c173]Anis Yazidi, B. John Oommen:
Novel Results on Random Walk-Jump Chains That Possess Tree-Based Transitions. CORES 2017: 43-52 - [c172]Abdolreza Shirvani, B. John Oommen:
Partitioning in signal processing using the object migration automaton and the pursuit paradigm. MLSP 2017: 1-6 - [c171]Rajan Thapa, Lei Jiao, B. John Oommen, Anis Yazidi:
Scheduling Domestic Shiftable Loads in Smart Grids: A Learning Automata-Based Scheme. SmartGIFT 2017: 58-68 - 2016
- [j158]Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo, Lei Jiao:
A formal proof of the 𝜀-optimality of discretized pursuit algorithms. Appl. Intell. 44(2): 282-294 (2016) - [j157]Lei Jiao, Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo:
Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach. Appl. Intell. 44(2): 307-321 (2016) - [j156]Anis Yazidi, B. John Oommen, Geir Horn, Ole-Christoffer Granmo:
Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments. Pattern Recognit. 60: 430-443 (2016) - [j155]Spencer Polk, B. John Oommen:
On Achieving History-Based Move Ordering in Adversarial Board Games Using Adaptive Data Structures. Trans. Comput. Collect. Intell. 22: 10-44 (2016) - [j154]B. John Oommen, Richard Khoury, Aron Schmidt:
Text Classification Using "Anti"-Bayesian Quantile Statistics-Based Classifiers. Trans. Comput. Collect. Intell. 25: 101-126 (2016) - [j153]Anis Yazidi, B. John Oommen:
Novel Discretized Weak Estimators Based on the Principles of the Stochastic Search on the Line Problem. IEEE Trans. Cybern. 46(12): 2732-2744 (2016) - [c170]César A. Astudillo, Jorge Poblete, Marina Resta, B. John Oommen:
A Cluster Analysis of Stock Market Data Using Hierarchical SOMs. Australasian Conference on Artificial Intelligence 2016: 101-112 - [c169]César A. Astudillo, Javier I. González, B. John Oommen, Anis Yazidi:
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers. Australasian Conference on Artificial Intelligence 2016: 175-182 - [c168]B. John Oommen, Sang-Woon Kim:
On the Foundations of Multinomial Sequence Based Estimation. ICCCI (1) 2016: 218-229 - [c167]Ratish Mohan, Anis Yazidi, Boning Feng, B. John Oommen:
Dynamic Ordering of Firewall Rules Using a Novel Swapping Window-based Paradigm. ICCNS 2016: 11-20 - [c166]B. John Oommen, Sang-Woon Kim:
Multinomial Sequence Based Estimation Using Contiguous Subsequences of Length Three. ICIAR 2016: 243-253 - [c165]Anis Yazidi, Hugo Lewi Hammer, B. John Oommen:
"Anti-Bayesian" Flat and Hierarchical Clustering Using Symmetric Quantiloids. IEA/AIE 2016: 56-67 - [c164]Hanane Tavasoli, B. John Oommen, Anis Yazidi:
On the Online Classification of Data Streams Using Weak Estimators. IEA/AIE 2016: 68-79 - [c163]Spencer Polk, B. John Oommen:
Challenging Established Move Ordering Strategies with Adaptive Data Structures. IEA/AIE 2016: 862-872 - [c162]Akaki Jobava, Anis Yazidi, B. John Oommen, Kyrre M. Begnum:
Achieving Intelligent Traffic-Aware Consolidation of Virtual Machines in a Data Center Using Learning Automata. NTMS 2016: 1-5 - 2015
- [j152]Yifeng Li, B. John Oommen, Alioune Ngom, Luis Rueda:
Pattern classification using a new border identification paradigm: The nearest border technique. Neurocomputing 157: 105-117 (2015) - [c161]Nathan Bell, B. John Oommen:
Particle Field Optimization: A New Paradigm for Swarm Intelligence. AAMAS 2015: 257-265 - [c160]Spencer Polk, B. John Oommen:
Space and depth-related enhancements of the history-ADS strategy in game playing. CIG 2015: 322-327 - [c159]Anis Yazidi, B. John Oommen:
Solving Stochastic Root-Finding with adaptive d-ary search. EAIS 2015: 1-8 - [c158]B. John Oommen, Richard Khoury, Aron Schmidt:
Text Classification Using Novel "Anti-Bayesian" Techniques. ICCCI (1) 2015: 1-15 - [c157]Spencer Polk, B. John Oommen:
Enhancing History-Based Move Ordering in Game Playing Using Adaptive Data Structures. ICCCI (1) 2015: 225-235 - [c156]Spencer Polk, B. John Oommen:
Novel AI Strategies for Multi-Player Games at Intermediate Board States. IEA/AIE 2015: 33-42 - [c155]César A. Astudillo, B. John Oommen:
Pattern Recognition using the TTOCONROT. IEA/AIE 2015: 435-444 - [c154]Hugo Lewi Hammer, Anis Yazidi, B. John Oommen:
A Novel Clustering Algorithm Based on a Non-parametric "Anti-Bayesian" Paradigm. IEA/AIE 2015: 536-545 - [c153]Anis Yazidi, B. John Oommen, Morten Goodwin Olsen:
On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth. WI-IAT (2) 2015: 104-111 - [i1]César A. Astudillo, B. John Oommen:
Self Organizing Maps Whose Topologies Can Be Learned With Adaptive Binary Search Trees Using Conditional Rotations. CoRR abs/1506.02750 (2015) - 2014
- [j151]Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen, Lei Jiao:
A formal proof of the ε-optimality of absorbing continuous pursuit algorithms using the theory of regular functions. Appl. Intell. 41(3): 974-985 (2014) - [j150]Qin Ke, B. John Oommen:
Logistic Neural Networks: Their chaotic and pattern recognition properties. Neurocomputing 125: 184-194 (2014) - [j149]César A. Astudillo, B. John Oommen:
Topology-oriented self-organizing maps: a survey. Pattern Anal. Appl. 17(2): 223-248 (2014) - [j148]B. John Oommen, Anu Thomas:
"Anti-Bayesian" parametric pattern classification using order statistics criteria for some members of the exponential family. Pattern Recognit. 47(1): 40-55 (2014) - [j147]César A. Astudillo, B. John Oommen:
Self-organizing maps whose topologies can be learned with adaptive binary search trees using conditional rotations. Pattern Recognit. 47(1): 96-113 (2014) - [j146]Anu Thomas, B. John Oommen:
Corrigendum to three papers that deal with "Anti"-Bayesian Pattern Recognition [Pattern Recognition]. Pattern Recognit. 47(6): 2301-2302 (2014) - [j145]Rokhsareh Sakhravi, Masoud T. Omran, B. John Oommen:
On the Existence and Heuristic Computation of the Solution for the Commons Game. Trans. Comput. Collect. Intell. 14: 71-99 (2014) - [j144]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen, Morten Goodwin Olsen:
A Novel Strategy for Solving the Stochastic Point Location Problem Using a Hierarchical Searching Scheme. IEEE Trans. Cybern. 44(11): 2202-2220 (2014) - [c152]Anis Yazidi, B. John Oommen, Ole-Christoffer Granmo, Morten Goodwin:
On Utilizing Stochastic Non-linear Fractional Bin Packing to Resolve Distributed Web Crawling. CSE 2014: 32-37 - [c151]Lei Jiao, Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen:
A Bayesian Learning Automata-Based Distributed Channel Selection Scheme for Cognitive Radio Networks. IEA/AIE (2) 2014: 48-57 - [c150]Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo, Lei Jiao:
Using the Theory of Regular Functions to Formally Prove the ε-Optimality of Discretized Pursuit Learning Algorithms. IEA/AIE (1) 2014: 379-388 - [c149]Ke Qin, B. John Oommen:
Cryptanalysis of a Cryptographic Algorithm that Utilizes Chaotic Neural Networks. ISCIS 2014: 167-174 - [c148]César A. Astudillo, B. John Oommen:
Fast BMU Search in SOMs Using Random Hyperplane Trees. PRICAI 2014: 39-51 - 2013
- [j143]Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen:
On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata. Appl. Intell. 39(4): 782-792 (2013) - [j142]Aleksander Stensby, B. John Oommen, Ole-Christoffer Granmo:
The Use of Weak estimators to Achieve Language Detection and Tracking in Multilingual Documents. Int. J. Pattern Recognit. Artif. Intell. 27(4) (2013) - [j141]B. John Oommen, Ebaa Fayyoumi:
On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases. Pattern Anal. Appl. 16(1): 99-116 (2013) - [j140]César A. Astudillo, B. John Oommen:
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs. Pattern Recognit. 46(1): 293-304 (2013) - [j139]Anu Thomas, B. John Oommen:
The fundamental theory of optimal "Anti-Bayesian" parametric pattern classification using order statistics criteria. Pattern Recognit. 46(1): 376-388 (2013) - [j138]Anu Thomas, B. John Oommen:
Order statistics-based parametric classification for multi-dimensional distributions. Pattern Recognit. 46(12): 3472-3482 (2013) - [j137]Ke Qin, B. John Oommen:
Ideal Chaotic Pattern Recognition Is Achievable: The Ideal-M-AdNN - Its Design and Properties. Trans. Comput. Collect. Intell. 11: 22-51 (2013) - [j136]Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen:
Learning-Automaton-Based Online Discovery and Tracking of Spatiotemporal Event Patterns. IEEE Trans. Cybern. 43(3): 1118-1130 (2013) - [j135]B. John Oommen, M. Khaled Hashem:
Modeling the "Learning Process" of the Teacher in a Tutorial-Like System Using Learning Automata. IEEE Trans. Cybern. 43(6): 2020-2031 (2013) - [c147]Anu Thomas, B. John Oommen:
Ultimate Order Statistics-Based Prototype Reduction Schemes. Australasian Conference on Artificial Intelligence 2013: 421-433 - [c146]Yifeng Li, B. John Oommen, Alioune Ngom, Luis Rueda:
A New Paradigm for Pattern Classification: Nearest Border Techniques. Australasian Conference on Artificial Intelligence 2013: 441-446 - [c145]Anu Thomas, B. John Oommen:
A Novel Border Identification Algorithm Based on an "Anti-Bayesian" Paradigm. CAIP (1) 2013: 196-203 - [c144]Anu Thomas, B. John Oommen:
On Achieving Near-Optimal "Anti-Bayesian" Order Statistics-Based Classification for Asymmetric Exponential Distributions. CAIP (1) 2013: 368-376 - [c143]Anu Thomas, B. John Oommen:
Classification of Multi-dimensional Distributions Using Order Statistics Criteria. CORES 2013