- CSB - MetaPress - Google - Bing - Yahoo
 | 2013 | |
|---|---|---|
| j117 |      | 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) |
| j116 | | César A. Astudillo, B. John Oommen: On achieving semi-supervised pattern recognition by utilizing tree-based SOMs. Pattern Recognition 46(1): 293-304 (2013) |
| j115 | | A. Thomas, B. John Oommen: The fundamental theory of optimal "Anti-Bayesian" parametric pattern classification using order statistics criteria. Pattern Recognition 46(1): 376-388 (2013) |
| 2012 | ||
| j114 | | Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen: Service selection in stochastic environments: a learning-automaton based solution. Appl. Intell. 36(3): 617-637 (2012) |
| j113 | | Ke Qin, B. John Oommen: The entire range of Chaotic pattern recognition properties possessed by the Adachi neural network. Intelligent Decision Technologies 6(1): 27-41 (2012) |
| j112 | | B. John Oommen, Anis Yazidi, Ole-Christoffer Granmo: An Adaptive Approach to Learning the Preferences of Users in a Social Network Using Weak Estimators. JIPS 8(2): 191-212 (2012) |
| j111 | | Sang-Woon Kim, B. John Oommen: On using prototype reduction schemes to optimize locally linear reconstruction methods. Pattern Recognition 45(1): 498-511 (2012) |
| j110 | | Dragos Calitoiu, B. John Oommen, Doron Nussbaum: Large-scale neuro-modeling for understanding and explaining some brain-related chaotic behavior. Simulation 88(11): 1316-1337 (2012) |
| j109 | | Colin Bellinger, B. John Oommen: On the Pattern Recognition and Classification of Stochastically Episodic Events. T. Computational Collective Intelligence 6: 1-35 (2012) |
| j108 | | B. John Oommen, M. Khaled Hashem: Modeling a Teacher in a Tutorial-like System Using Learning Automata. T. Computational Collective Intelligence 8: 37-62 (2012) |
| c125 | | A. Thomas, B. John Oommen: Optimal "Anti-Bayesian" Parametric Pattern Classification Using Order Statistics Criteria. CIARP 2012: 1-13 |
| c124 | | Rokhsareh Sakhravi, Masoud T. Omran, B. John Oommen: A Fast Heuristic Solution for the Commons Game. DCAI 2012: 81-90 |
| c123 | | A. Thomas, B. John Oommen: Optimal "Anti-Bayesian" Parametric Pattern Classification for the Exponential Family Using Order Statistics Criteria. ICIAR (1) 2012: 11-18 |
| c122 | | Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen: A Stochastic Search on the Line-Based Solution to Discretized Estimation. IEA/AIE 2012: 764-773 |
| c121 | | Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen, Morten Goodwin Olsen: A Hierarchical Learning Scheme for Solving the Stochastic Point Location Problem. IEA/AIE 2012: 774-783 |
| c120 | | Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen: Discretized Bayesian Pursuit - A New Scheme for Reinforcement Learning. IEA/AIE 2012: 784-793 |
| 2011 | ||
| j107 | | Ole-Christoffer Granmo, B. John Oommen: Learning automata-based solutions to the optimal web polling problem modelled as a nonlinear fractional knapsack problem. Eng. Appl. of AI 24(7): 1238-1251 (2011) |
| j106 | | César A. Astudillo, B. John Oommen: Imposing tree-based topologies onto self organizing maps. Inf. Sci. 181(18): 3798-3815 (2011) |
| j105 | | Justin Zhan, B. John Oommen, Johanna Crisostomo: Anomaly Detection in Dynamic Systems Using Weak Estimators. ACM Trans. Internet Techn. 11(1): 3 (2011) |
| j104 | | Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen, Martin Gerdes, Frank Reichert: A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments. Wireless Personal Communications 61(3): 543-566 (2011) |
| c119 | | Colin Bellinger, B. John Oommen: A New Frontier in Novelty Detection: Pattern Recognition of Stochastically Episodic Events. ACIIDS (1) 2011: 435-444 |
| c118 | | César A. Astudillo, B. John Oommen: Semi-Supervised Classification Using Tree-Based Self-Organizing Maps. Australasian Conference on Artificial Intelligence 2011: 21-30 |
| c117 | | Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen: Tracking the Preferences of Users Using Weak Estimators. Australasian Conference on Artificial Intelligence 2011: 799-808 |
| c116 | | Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen: On the analysis of a new Markov chain which has applications in AI and machine learning. CCECE 2011: 1553-1558 |
| c115 | | Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen: A New Tool for the Modeling of AI and Machine Learning Applications: Random Walk-Jump Processes. HAIS (1) 2011: 11-21 |
| c114 | | Petro Verkhogliad, B. John Oommen: Using Artificial Intelligence Techniques for Strategy Generation in the Commons Game. HAIS (1) 2011: 43-50 |
| c113 | | Xuan Zhang, Ole-Christoffer Granmo, B. John Oommen: The Bayesian Pursuit Algorithm: A New Family of Estimator Learning Automata. IEA/AIE (2) 2011: 522-531 |
| c112 | | Xuan Zhang, B. John Oommen, Ole-Christoffer Granmo: Generalized Bayesian Pursuit: A Novel Scheme for Multi-Armed Bernoulli Bandit Problems. EANN/AIAI (2) 2011: 122-131 |
| c111 | | Justin Zhan, B. John Oommen, Johanna Crisostomo: Anomaly detection using weak estimators. ISI 2011: 143-149 |
| c110 | | B. John Oommen: On Merging the Fields of Neural Networks and Adaptive Data Structures to Yield New Pattern Recognition Methodologies. PReMI 2011: 13-16 |
| 2010 | ||
| j103 | | B. John Oommen, M. Khaled Hashem: Modeling a Domain in a Tutorial-like System Using Learning Automata. Acta Cybern. 19(3): 635-653 (2010) |
| j102 | | Ole-Christoffer Granmo, B. John Oommen: Optimal sampling for estimation with constrained resources using a learning automaton-based solution for the nonlinear fractional knapsack problem. Appl. Intell. 33(1): 3-20 (2010) |
| j101 | | Luis Rueda, B. John Oommen, Claudio Henríquez: Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes. Pattern Recognition 43(7): 2456-2465 (2010) |
| j100 | | Eser Aygün, B. John Oommen, Zehra Cataltepe: Peptide classification using optimal and information theoretic syntactic modeling. Pattern Recognition 43(11): 3891-3899 (2010) |
| j99 | | Ebaa Fayyoumi, B. John Oommen: A survey on statistical disclosure control and micro-aggregation techniques for secure statistical databases. Softw., Pract. Exper. 40(12): 1161-1188 (2010) |
| j98 | | Ole-Christoffer Granmo, B. John Oommen: Solving Stochastic Nonlinear Resource Allocation Problems Using a Hierarchy of Twofold Resource Allocation Automata. IEEE Trans. Computers 59(4): 545-560 (2010) |
| j97 | | B. John Oommen, Sudip Misra: Fault-tolerant routing in adversarial mobile ad hoc networks: an efficient route estimation scheme for non-stationary environments. Telecommunication Systems 44(1-2): 159-169 (2010) |
| j96 | | Geir Horn, B. John Oommen: Solving Multiconstraint Assignment Problems Using Learning Automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B 40(1): 6-18 (2010) |
| j95 | | B. John Oommen, M. Khaled Hashem: Modeling a Student-Classroom Interaction in a Tutorial-Like System Using Learning Automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B 40(1): 29-42 (2010) |
| j94 | | Sudip Misra, B. John Oommen, Sreekeerthy Yanamandra, Mohammad S. Obaidat: Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution. IEEE Transactions on Systems, Man, and Cybernetics, Part B 40(1): 66-76 (2010) |
| j93 | | B. John Oommen, Ebaa Fayyoumi: On Utilizing Association and Interaction Concepts for Enhancing Microaggregation in Secure Statistical Databases. IEEE Transactions on Systems, Man, and Cybernetics, Part B 40(1): 198-207 (2010) |
| j92 | | B. John Oommen, M. Khaled Hashem: Modeling a Student's Behavior in a Tutorial-Like System Using Learning Automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B 40(2): 481-492 (2010) |
| c109 | | Petro Verkhogliad, B. John Oommen: Potential AI Strategies to Solve the Commons Game: A Position Paper. Canadian Conference on AI 2010: 352-356 |
| c108 | | Sang-Woon Kim, B. John Oommen: On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes. Australasian Conference on Artificial Intelligence 2010: 153-163 |
| c107 |  | Thomas Norheim, Terje Brådland, Ole-Christoffer Granmo, B. John Oommen: A Generic Solution to Multi-Armed Bernoulli Bandit Problems based on Random Sampling from Sibling Conjugate Priors. ICAART (1) 2010: 36-44 |
| c106 | | Dragos Calitoiu, B. John Oommen: On using Simulation and Stochastic Learning for Pattern Recognition When Training Data is Unavailable - The Case of Disease Outbreak. ICAART (1) 2010: 45-52 |
| c105 | | Anis Yazidi, Ole-Christoffer Granmo, B. John Oommen: A Learning Automata Based Solution to Service Selection in Stochastic Environments. IEA/AIE (3) 2010: 209-218 |
| c104 | | Anis Yazidi, Ole-Christoffer Granmo, Min Lin, Xifeng Wen, B. John Oommen, Martin Gerdes, Frank Reichert: Learning Automaton Based On-Line Discovery and Tracking of Spatio-temporal Event Patterns. PRICAI 2010: 327-338 |
| c103 | | Colin Bellinger, B. John Oommen: On simulating episodic events against a background of noise-like non-episodic events. SummerSim 2010: 452-460 |
| c102 | | Aleksander Stensby, B. John Oommen, Ole-Christoffer Granmo: Language Detection and Tracking in Multilingual Documents Using Weak Estimators. SSPR/SPR 2010: 600-609 |
| 2009 | ||
| j91 | | Sudip Misra, B. John Oommen: An efficient pursuit automata approach for estimating stable all-pairs shortest paths in stochastic network environments. Int. J. Communication Systems 22(4): 441-468 (2009) |
| j90 | | Qingxin Zhu, B. John Oommen: Estimation of distributions involving unobservable events: the case of optimal search with unknown Target Distributions. Pattern Anal. Appl. 12(1): 37-53 (2009) |
| j89 | | Sang-Woon Kim, B. John Oommen: On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures. Pattern Recognition 42(11): 2695-2704 (2009) |
| j88 | | Ke Qin, B. John Oommen: Adachi-Like Chaotic Neural Networks Requiring Linear-Time Computations by Enforcing a Tree-Shaped Topology. IEEE Transactions on Neural Networks 20(11): 1797-1809 (2009) |
| j87 | | Ebaa Fayyoumi, B. John Oommen: Achieving Microaggregation for Secure Statistical Databases Using Fixed-Structure Partitioning-Based Learning Automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B 39(5): 1192-1205 (2009) |
| c101 | | Sudip Misra, B. John Oommen, Sreekeerthy Yanamandra, Mohammad S. Obaidat: An adaptive learning-like solution of random early detection for congestion avoidance in computer networks. AICCSA 2009: 485-491 |
| c100 | | César A. Astudillo, B. John Oommen: On Using Adaptive Binary Search Trees to Enhance Self Organizing Maps. Australasian Conference on Artificial Intelligence 2009: 199-209 |
| c99 | | Justin Zhan, B. John Oommen, Johanna Crisostomo: Anomaly Detection in Dynamic Social Systems Using Weak Estimators. CSE (4) 2009: 18-25 |
| c98 | | Ole-Christoffer Granmo, B. John Oommen: A Hierarchy of Twofold Resource Allocation Automata Supporting Optimal Sampling. IEA/AIE 2009: 523-534 |
| c97 | | B. John Oommen, M. Khaled Hashem: Learning Automata Based Intelligent Tutorial-like System. KES (1) 2009: 360-373 |
| c96 | | Eser Aygün, B. John Oommen, Zehra Cataltepe: On Utilizing Optimal and Information Theoretic Syntactic Modeling for Peptide Classification. PRIB 2009: 24-35 |
| p2 | | B. John Oommen, Sudip Misra: Cybernetics and Learning Automata. Handbook of Automation 2009: 221-235 |
| p1 | | Ole-Christoffer Granmo, B. John Oommen: Learning Automata-Based Solutions to Stochastic Nonlinear Resource Allocation Problems. Intelligent Systems for Knowledge Management 2009: 1-30 |
| 2008 | ||
| j86 | | Dragos Calitoiu, B. John Oommen, Doron Nussbaum: Spikes annihilation in the Hodgkin-Huxley neuron. Biological Cybernetics 98(3): 239-257 (2008) |
| j85 | | Luis Rueda, B. John Oommen: An efficient compression scheme for data communication which uses a new family of self-organizing binary search trees. Int. J. Communication Systems 21(10): 1091-1120 (2008) |
| j84 | | B. John Oommen, Sang-Woon Kim, M. T. Samuel, Ole-Christoffer Granmo: A Solution to the Stochastic Point Location Problem in Metalevel Nonstationary Environments. IEEE Transactions on Systems, Man, and Cybernetics, Part B 38(2): 466-476 (2008) |
| j83 | | Sang-Woon Kim, B. John Oommen: On Using Prototype Reduction Schemes to Optimize Kernel-Based Fisher Discriminant Analysis. IEEE Transactions on Systems, Man, and Cybernetics, Part B 38(2): 564-570 (2008) |
| c95 | | B. John Oommen, Ebaa Fayyoumi: Enhancing Micro-Aggregation Technique by Utilizing Dependence-Based Information in Secure Statistical Databases. ACISP 2008: 404-418 |
| c94 | | Sang-Woon Kim, B. John Oommen: A Fast Computation of Inter-class Overlap Measures Using Prototype Reduction Schemes. Canadian Conference on AI 2008: 173-184 |
| c93 | | B. John Oommen, Ebaa Fayyoumi: An AI-Based Causal Strategy for Securing Statistical Databases Using Micro-aggregation. Australasian Conference on Artificial Intelligence 2008: 423-434 |
| c92 | | Luis Rueda, Claudio Henríquez, B. John Oommen: Chernoff-Based Multi-class Pairwise Linear Dimensionality Reduction. CIARP 2008: 301-308 |
| c91 | | Ole-Christoffer Granmo, B. John Oommen: A Hierarchy of Twofold Resource Allocation Automata Supporting Optimal Web Polling. IEA/AIE 2008: 347-358 |
| c90 | | B. John Oommen, Dragos Calitoiu: Modeling and simulating a disease outbreak by learning a contagion parameter-based model. SpringSim 2008: 547-555 |
| c89 | | Dragos Calitoiu, Doron Nussbaum, B. John Oommen: Large scale modeling of the piriform cortex for analyzing antiepileptic effects. SpringSim 2008: 599-608 |
| c88 | | Ke Qin, B. John Oommen: Chaotic Pattern Recognition: The Spectrum of Properties of the Adachi Neural Network. SSPR/SPR 2008: 540-550 |
| 2007 | ||
| j82 | | Abdelrahman Amer, B. John Oommen: A Novel Framework for Self-Organizing Lists in Environments with Locality of Reference: Lists-on-Lists. Comput. J. 50(2): 186-196 (2007) |
| j81 | | B. John Oommen, Ghada Hany Badr: Breadth-first search strategies for trie-based syntactic pattern recognition. Pattern Anal. Appl. 10(1): 1-13 (2007) |
| j80 | | Dragos Calitoiu, B. John Oommen, Doron Nussbaum: Periodicity and stability issues of a chaotic pattern recognition neural network. Pattern Anal. Appl. 10(3): 175-188 (2007) |
| j79 | | Sang-Woon Kim, B. John Oommen: On using prototype reduction schemes to optimize dissimilarity-based classification. Pattern Recognition 40(11): 2946-2957 (2007) |
| j78 | | B. John Oommen, Sang-Woon Kim, Geir Horn: On the estimation of independent binomial random variables using occurrence and sequential information. Pattern Recognition 40(11): 3263-3276 (2007) |
| j77 | | B. John Oommen, Sudip Misra, Ole-Christoffer Granmo: Routing Bandwidth-Guaranteed Paths in MPLS Traffic Engineering: A Multiple Race Track Learning Approach. IEEE Trans. Computers 56(7): 959-976 (2007) |
| j76 | | Pradeep K. Atrey, Mohan S. Kankanhalli, B. John Oommen: Goal-oriented optimal subset selection of correlated multimedia streams. TOMCCAP 3(1) (2007) |
| j75 | | Ole-Christoffer Granmo, B. John Oommen, Svein Arild Myrer, Morten Goodwin Olsen: Learning Automata-Based Solutions to the Nonlinear Fractional Knapsack Problem With Applications to Optimal Resource Allocation. IEEE Transactions on Systems, Man, and Cybernetics, Part B 37(1): 166-175 (2007) |
| j74 | | Dragos Calitoiu, B. John Oommen, Doron Nussbaum: Desynchronizing a Chaotic Pattern Recognition Neural Network to Model Inaccurate Perception. IEEE Transactions on Systems, Man, and Cybernetics, Part B 37(3): 692-704 (2007) |
| c87 | | Dragos Calitoiu, B. John Oommen, Doron Nussbaum: Analytic Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation. Canadian Conference on AI 2007: 320-331 |
| c86 | | Sudip Misra, B. John Oommen: The Pursuit Automaton Approach for Estimating All-Pairs Shortest Paths in Dynamically Changing Networks. AINA Workshops (1) 2007: 124-129 |
| c85 | | Ole-Christoffer Granmo, B. John Oommen: On Using a Hierarchy of Twofold Resource Allocation Automata to Solve Stochastic Nonlinear Resource Allocation Problems. Australian Conference on Artificial Intelligence 2007: 36-47 |
| c84 | | Dragos Calitoiu, B. John Oommen, Doron Nussbaum: Some Analysis on the Network of Bursting Neurons: Quantifying Behavioral Synchronization. Australian Conference on Artificial Intelligence 2007: 110-119 |
| c83 | | Dragos Calitoiu, B. John Oommen, Doron Nussbaum: Numerical Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation. BVAI 2007: 378-387 |
| c82 | | B. John Oommen, Ole-Christoffer Granmo, Asle Pedersen: Using Stochastic AI Techniques to Achieve Unbounded Resolution in Finite Player Goore Games and its Applications. CIG 2007: 161-167 |
| c81 | | B. John Oommen, Ebaa Fayyoumi: A Novel Method for Micro-Aggregation in Secure Statistical Databases Using Association and Interaction. ICICS 2007: 126-140 |
| c80 | | M. Khaled Hashem, B. John Oommen: On Using Learning Automata to Model a Student's Behavior in a Tutorial-like System. IEA/AIE 2007: 813-822 |
| c79 | | B. John Oommen, Sang-Woon Kim, Mathew Samuel, Ole-Christoffer Granmo: Stochastic Point Location in Non-stationary Environments and Its Applications. IEA/AIE 2007: 845-854 |
| c78 | | M. Khaled Hashem, B. John Oommen: Using learning automata to model the behavior of a teacher in a tutorial-like system. SMC 2007: 76-82 |
| c77 | | M. Khaled Hashem, B. John Oommen: Using learning automata to model a student-classroom interaction in a tutorial-like system. SMC 2007: 1177-1182 |
| 2006 | ||
| j73 | | Luis Rueda, B. John Oommen: A fast and efficient nearly-optimal adaptive Fano coding scheme. Inf. Sci. 176(12): 1656-1683 (2006) |
| j72 | | Ghada Badr, B. John Oommen: A novel look-ahead optimization strategy for trie-based approximate string matching. Pattern Anal. Appl. 9(2-3): 177-187 (2006) |
| j71 | | Sang-Woon Kim, B. John Oommen: Prototype reduction schemes applicable for non-stationary data sets. Pattern Recognition 39(2): 209-222 (2006) |
| j70 | | B. John Oommen, Luis Rueda: Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments. Pattern Recognition 39(3): 328-341 (2006) |
| j69 | | Sudip Misra, B. John Oommen: An Efficient Dynamic Algorithm for Maintaining All-Pairs Shortest Paths in Stochastic Networks. IEEE Trans. Computers 55(6): 686-702 (2006) |
| c76 | | Xavier Hilaire, B. John Oommen: The averaged mappings problem: statement, applications, and approximate solution. ACM Southeast Regional Conference 2006: 24-29 |
| c75 | | Ebaa Fayyoumi, B. John Oommen: On Optimizing the k-Ward Micro-aggregation Technique for Secure Statistical Databases. ACISP 2006: 324-335 |
| c74 | | Denis V. Batalov, B. John Oommen: Turning Lights Out with DQ-Learning. Artificial Intelligence and Applications 2006: 451-456 |
| c73 | | B. John Oommen, Ole-Christoffer Granmo, Asle Pedersen: Empirical Verification of a Strategy for Unbounded Resolution in Finite Player Goore Games. Australian Conference on Artificial Intelligence 2006: 1252-1258 |
| c72 | | B. John Oommen, Jing Chen: On Utilizing Attribute Cardinality Maps to Enhance Query Optimization in the Oracle Database System. ICEIS (1) 2006: 23-35 |
| c71 | | B. John Oommen, Jing Chen: On Enhancing Query Optimization in the Oracle Database System by Utilizing Attribute Cardinality Maps. ICEIS (Selected Papers) 2006: 38-71 |
| c70 | | Sang-Woon Kim, B. John Oommen: On Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes. ICIAR (1) 2006: 15-28 |
| c69 | | B. John Oommen, Sudip Misra, Ole-Christoffer Granmo: A Stochastic Random-Races Algorithm for Routing in MPLS Traffic Engineering. INFOCOM 2006 |
| c68 | | Ebaa Fayyoumi, B. John Oommen: A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases. Privacy in Statistical Databases 2006: 114-128 |
| c67 | | |
| c66 | | B. John Oommen, Sang-Woon Kim, Geir Horn: On the Theory and Applications of Sequence Based Estimation of Independent Binomial Random Variables. SSPR/SPR 2006: 8-21 |
| c65 | | Sang-Woon Kim, B. John Oommen: On Optimizing Kernel-Based Fisher Discriminant Analysis Using Prototype Reduction Schemes. SSPR/SPR 2006: 826-834 |
| c64 | | Abdelrahman Amer, B. John Oommen: Lists on Lists: A Framework for Self-organizing Lists in Environments with Locality of Reference. WEA 2006: 109-120 |
| c63 | | B. John Oommen, Sudip Misra: A Fault-Tolerant Routing Algorithm for Mobile Ad Hoc Networks Using a Stochastic Learning-Based Weak Estimation Procedure. WiMob 2006: 31-37 |
| 2005 | ||
| j68 | | B. John Oommen, Luís G. Rueda: A formal analysis of why heuristic functions work. Artif. Intell. 164(1-2): 1-22 (2005) |
| j67 | | Ghada Hany Badr, B. John Oommen: Self-Adjusting of Ternary Search Tries Using Conditional Rotations and Randomized Heuristics. Comput. J. 48(2): 200-219 (2005) |
| j66 | | Sang-Woon Kim, B. John Oommen: On Utilizing Search Methods to Select Subspace Dimensions for Kernel-Based Nonlinear Subspace Classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 27(1): 136-141 (2005) |
| j65 | | Sang-Woon Kim, B. John Oommen: On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods. IEEE Trans. Pattern Anal. Mach. Intell. 27(3): 455-460 (2005) |
| j64 | | Sudip Misra, B. John Oommen: Dynamic algorithms for the shortest path routing problem: learning automata-based solutions. IEEE Transactions on Systems, Man, and Cybernetics, Part B 35(6): 1179-1192 (2005) |
| c62 | | Ghada Badr, B. John Oommen: On using conditional rotations and randomized heuristics for self-organizing ternary search tries. ACM Southeast Regional Conference (1) 2005: 109-115 |
| c61 | | Sang-Woon Kim, B. John Oommen: Time-Varying Prototype Reduction Schemes Applicable for Non-stationary Data Sets. Australian Conference on Artificial Intelligence 2005: 614-623 |
| c60 | | Dragos Calitoiu, B. John Oommen, Doron Nussbaum: Neural Network-Based Chaotic Pattern Recognition - Part 2: Stability and Algorithmic Issues. CORES 2005: 3-16 |
| c59 | | Ghada Badr, B. John Oommen: A Look-Ahead Branch and Bound Pruning Scheme for Trie-Based Approximate String Matching. CORES 2005: 87-94 |
| c58 | | Ghada Badr, B. John Oommen: Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies. ICAPR (1) 2005: 1-17 |
| c57 | | Geir Horn, B. John Oommen: A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem. IPDPS 2005 |
| c56 | | Sudip Misra, B. John Oommen: New Algorithms for Maintaining All-Pairs Shortest Paths. ISCC 2005: 116-121 |
| c55 | | Luís G. Rueda, B. John Oommen: Efficient Adaptive Data Compression Using Fano Binary Search Trees. ISCIS 2005: 768-779 |
| c54 | | B. John Oommen, Luís G. Rueda: On Utilizing Stochastic Learning Weak Estimators for Training and Classification of Patterns with Non-stationary Distributions. KI 2005: 107-120 |
| c53 | | Dragos Calitoiu, B. John Oommen, Dorin Nusbaumm: Modeling Inaccurate Perception: Desynchronization Issues of a Chaotic Pattern Recognition Neural Network. SCIA 2005: 821-830 |
| 2004 | ||
| j63 | | Sudip Misra, B. John Oommen: GPSPA: a new adaptive algorithm for maintaining shortest path routing trees in stochastic networks. Int. J. Communication Systems 17(10): 963-984 (2004) |
| j62 | | Luís G. Rueda, B. John Oommen: A nearly-optimal Fano-based coding algorithm. Inf. Process. Manage. 40(2): 257-268 (2004) |
| j61 | | M. Ouerd, B. John Oommen, Stan Matwin: A formal approach to using data distributions for building causal polytree structures. Inf. Sci. 168(1-4): 111-132 (2004) |
| j60 | | Sang-Woon Kim, B. John Oommen: On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods. Pattern Recognition 37(2): 227-239 (2004) |
| j59 | | Sang-Woon Kim, B. John Oommen: Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets. IEEE Transactions on Systems, Man, and Cybernetics, Part B 34(3): 1384-1397 (2004) |
| c52 | | B. John Oommen, Jack R. Zgierski, Doron Nussbaum: Deterministic Majority filters applied to stochastic sorting. ACM Southeast Regional Conference 2004: 228-233 |
| c51 | | Sudip Misra, B. John Oommen: Adaptive Algorithms for Routing and Traffic Engineering in Stochastic Networks. AAAI 2004: 993-994 |
| c50 | | Luís G. Rueda, B. John Oommen: On Families of New Adaptive Compression Algorithms Suitable for Time-Varying Source Data. ADVIS 2004: 234-244 |
| c49 | | Sang-Woon Kim, B. John Oommen: Selecting Subspace Dimensions for Kernel-Based Nonlinear Subspace Classifiers Using Intelligent Search Methods. Australian Conference on Artificial Intelligence 2004: 1115-1121 |
| c48 | | Sudip Misra, B. John Oommen: Stochastic Learning Automata-Based Dynamic Algorithms for the Single Source Shortest Path Problem. IEA/AIE 2004: 239-248 |
| c47 | | Sudip Misra, B. John Oommen: Generalized pursuit learning algorithms for shortest path routing tree computation. ISCC 2004: 891-896 |
| c46 | | B. John Oommen, Jack R. Zgierski, Doron Nussbaum: Stochastic Sorting Using Deterministic Consecutive and Leader Filters. MSV/AMCS 2004: 399-405 |
| c45 | | B. John Oommen: Recent Results on Learning from Stochastic Teachers and Compulsive Liars/Con-Men. PRIS 2004: 4 |
| c44 | | |
| c43 | | B. John Oommen, Ghada Badr: Dictionary-Based Syntactic Pattern Recognition Using Tries. SSPR/SPR 2004: 251-259 |
| c42 | | B. John Oommen, Luís G. Rueda: A New Family of Weak Estimators for Training in Non-stationary Distributions. SSPR/SPR 2004: 644-652 |
| 2003 | ||
| j58 | | Sang-Woon Kim, B. John Oommen: A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Anal. Appl. 6(3): 232-244 (2003) |
| j57 | | Luís G. Rueda, B. John Oommen: On optimal pairwise linear classifiers for normal distributions: the d-dimensional case. Pattern Recognition 36(1): 13-23 (2003) |
| j56 | | Sang-Woon Kim, B. John Oommen: Enhancing prototype reduction schemes with LVQ3-type algorithms. Pattern Recognition 36(5): 1083-1093 (2003) |
| j55 | | Necati Aras, I. Kuban Altinel, B. John Oommen: A Kohonen-like decomposition method for the Euclidean traveling salesman problem-KNIES_DECOMPOSE. IEEE Transactions on Neural Networks 14(4): 869-890 (2003) |
| j54 | | B. John Oommen, Murali Thiyagarajah: Benchmarking attribute cardinality maps for database systems using the TPC-D specifications. IEEE Transactions on Systems, Man, and Cybernetics, Part B 33(6): 913-924 (2003) |
| c41 | | Ouerd Messaouda, B. John Oommen, Stan Matwin: Enhancing Caching in Distributed Databases Using Intelligent Polytree Representations. Canadian Conference on AI 2003: 498-504 |
| c40 | | B. John Oommen, Govindachari Raghunath, Benjamin Kuipers: On How to Learn from a Stochastic Teacher or a Stochastic Compulsive Liar of Unknown Identity. Australian Conference on Artificial Intelligence 2003: 24-40 |
| c39 | | Sang-Woon Kim, B. John Oommen: On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods. Australian Conference on Artificial Intelligence 2003: 783-795 |
| c38 | | B. John Oommen, Jing Chen: A new histogram method for sparse attributes: the averaged rectangular attribute cardinality map. ISICT 2003: 119-125 |
| c37 | | |
| 2002 | ||
| j53 | | B. John Oommen, Luís G. Rueda: The Efficiency of Histogram-like Techniques for Database Query Optimization. Comput. J. 45(5): 494-510 (2002) |
| j52 | | Luís G. Rueda, B. John Oommen: On Optimal Pairwise Linear Classifiers for Normal Distributions: The Two-Dimensional Case. IEEE Trans. Pattern Anal. Mach. Intell. 24(2): 274-280 (2002) |
| j51 | | Gopal Racherla, Sridhar Radhakrishnan, B. John Oommen: Enhanced layered segment trees: a pragmatic data structure for real-time processing of geometric objects. Pattern Recognition 35(10): 2303-2309 (2002) |
| j50 | | M. Agache, B. John Oommen: Generalized pursuit learning schemes: new families of continuous and discretized learning automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B 32(6): 738-749 (2002) |
| j49 | | B. John Oommen, T. Dale Roberts: Discretized learning automata solutions to the capacity assignment problem for prioritized networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B 32(6): 821-831 (2002) |
| c36 | | Sang-Woon Kim, B. John Oommen: Optimizing Kernel-Based Nonlinear Subspace Methods Using Prototype Reduction Schemes. Australian Joint Conference on Artificial Intelligence 2002: 155-166 |
| c35 | | B. John Oommen, Luís G. Rueda: Using Pattern Recognition Techniques to Derive a Formal Analysis of Why Heuristics Functions Work. PRIS 2002: 45-58 |
| c34 | | Sang-Woon Kim, B. John Oommen: On Utilizing LVQ3-Type Algorithms to Enhance Prototype Reduction Schemes. PRIS 2002: 242-256 |
| c33 | | Sang-Woon Kim, B. John Oommen: Recursive Prototype Reduction Schemes Applicable for Large Data Sets. SSPR/SPR 2002: 528-537 |
| 2001 | ||
| j48 | | B. John Oommen, R. K. S. Loke: On the Pattern Recognition of Noisy Subsequence Trees. IEEE Trans. Pattern Anal. Mach. Intell. 23(9): 929-946 (2001) |
| j47 | | B. John Oommen, M. Agache: Continuous and discretized pursuit learning schemes: various algorithms and their comparison. IEEE Transactions on Systems, Man, and Cybernetics, Part B 31(3): 277-287 (2001) |
| c32 | | Luís G. Rueda, B. John Oommen: Resolving Minsky's Paradox: The d-Dimensional Normal Distribution Case. Australian Joint Conference on Artificial Intelligence 2001: 25-36 |
| c31 | | B. John Oommen, Luís G. Rueda: Histogram Methods in Query Optimization: The Relation between Accuracy and Optimality. DASFAA 2001: 320-326 |
| c30 | | Gopal Racherla, Sridhar Radhakrishnan, B. John Oommen: A New Geometric Tool for Pattern Recognition - An Algorithm for Real Time Insertion of Layered Segment Trees. ICAPR 2001: 212-221 |
| c29 | | |
| 2000 | ||
| j46 | | B. John Oommen, T. Dale Roberts: Continuous Learning Automata Solutions to the Capacity Assignment Problem. IEEE Trans. Computers 49(6): 608-620 (2000) |
| c28 | | Necati Aras, I. Kuban Altinel, B. John Oommen: A Kohonen-like Decomposition Method for the Traveling Salesman Problem: KNIESDECOMPOSE. ECAI 2000: 261-265 |
| c27 | | B. John Oommen, Luis Rueda: An Empirical Comparison of Histogram-Like Techniques for Query Optimization. ICEIS 2000: 71-78 |
| c26 | | B. John Oommen, Murali Thiyagarajah: Query Result Size Estimation Using the Trapezoidal Attribute Cardinality Map. IDEAS 2000: 236-242 |
| c25 | | M. Ouerd, B. John Oommen, Stan Matwin: A Formalism for Building Causal Polytree Structures Using Data Distributions. ISMIS 2000: 629-637 |
| c24 | | Luis Rueda, B. John Oommen: The Foundational Theory of Optimal Bayesian Pairwise Linear Classifiers. SSPR/SPR 2000: 581-590 |
| 1999 | ||
| j45 | | Necati Aras, B. John Oommen, I. Kuban Altinel: The Kohonen network incorporating explicit statistics and its application to the travelling salesman problem. Neural Networks 12(9): 1273-1284 (1999) |
| j44 | | B. John Oommen, R. K. S. Loke: Designing syntactic pattern classifiers using vector quantization and parametric string editing. IEEE Transactions on Systems, Man, and Cybernetics, Part B 29(6): 881-888 (1999) |
| c23 | | Murali Thiyagarajah, B. John Oommen: On Benchmarking Attribute Cardinality Maps for Database Systems Using the TPC-D Specification. DEXA 1999: 292-301 |
| c22 | | Murali Thiyagarajah, B. John Oommen: Prototype Validation of the Trapezoidal Attribute Cardinality Map for Query Optimization in Database Systems. ICEIS 1999: 156-162 |
| c21 | | B. John Oommen, Murali Thiyagarajah: Query Result Size Estimation Using a Novel Histogram-like Technique: The Rectangular Attribute Cardinality Map. IDEAS 1999: 3-15 |
| c20 | | B. John Oommen, T. Dale Roberts: On Solving the Capacity Assignment Problem Using Continous Learning Automata. IEA/AIE 1999: 622-631 |
| 1998 | ||
| j43 | | B. John Oommen, Rangasami L. Kashyap: A formal theory for optimal and information theoretic syntactic pattern recognition. Pattern Recognition 31(8): 1159-1177 (1998) |
| j42 | | B. John Oommen, I. Kuban Altinel, Necati Aras: Discrete vector quantization for arbitrary distance function estimation. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(4): 496-510 (1998) |
| j41 | | B. John Oommen, Govindachari Raghunath: Automata learning and intelligent tertiary searching for stochastic point location. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(6): 947-954 (1998) |
| c19 | | B. John Oommen, T. Dale Roberts: A Fast Efficient Solution to the Capacity Assignment Problem Using Discretized Learning Automata. IEA/AIE (Vol. 2) 1998: 56-65 |
| c18 | | B. John Oommen, R. K. S. Loke: The Noisy Subsequence Tree Recognition Problem. SSPR/SPR 1998: 169-180 |
| 1997 | ||
| j40 | | I. Kuban Altinel, B. John Oommen, Necati Aras: Vector Quantization for Arbitrary Distance Function Estimation. INFORMS Journal on Computing 9(4): 439-451 (1997) |
| j39 | | Thai B. Nguyen, B. John Oommen: Moment-Preserving Piecewise Linear Approximations of Signals and Images. IEEE Trans. Pattern Anal. Mach. Intell. 19(1): 84-91 (1997) |
| j38 | | B. John Oommen, Richard K. S. Loke: Pattern recognition of strings with substitutions, insertions, deletions and generalized transpositions. Pattern Recognition 30(5): 789-800 (1997) |
| j37 | | B. John Oommen, Edward V. de St. Croix: String taxonomy using learning automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B 27(2): 354-365 (1997) |
| j36 | | B. John Oommen: Stochastic searching on the line and its applications to parameter learning in nonlinear optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B 27(4): 733-739 (1997) |
| c17 | | B. John Oommen, Juan Dong: Generalized Swap-with-Parent Schemes for Self-Organizing Sequential Linear Lists. ISAAC 1997: 414-423 |
| c16 | | Qingxin Zhu, B. John Oommen: On the Optimal Search Problem: The Case when the Target Distribution is Unknown. SCCC 1997: 268-277 |
| 1996 | ||
| j35 | | B. John Oommen, K. Zhang: The Normalized String Editing Problem Revisited. IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 669-672 (1996) |
| j34 | | B. John Oommen, Edward V. de St. Croix: Graph Partitioning Using Learning Automata. IEEE Trans. Computers 45(2): 195-208 (1996) |
| j33 | | B. John Oommen, K. Zhang, William Lee: Numerical Similarity and Dissimilarity Measures Between Two Trees. IEEE Trans. Computers 45(12): 1426-1434 (1996) |
| c15 | | B. John Oommen, R. K. S. Loke: Optimal and Information Theoretic Syntactic Pattern Recognition Involving Traditional and Transposition Errors. FSTTCS 1996: 224-237 |
| c14 | | B. John Oommen, Rangasami L. Kashyap: Optimal and Information Theoretic Syntactic Pattern Recognition for Traditional Errors. SSPR 1996: 11-20 |
| 1995 | ||
| j32 | | B. John Oommen: String Alignment with Substitution, Insertion, Deletion, Squashing and Expansion Operations. Inf. Sci. 83(1&2): 89-107 (1995) |
| j31 | | S. T. Sum, B. John Oommen: Mixture decomposition for distributions from the exponential family using a generalized method of moments. IEEE Transactions on Systems, Man, and Cybernetics 25(7): 1139-1149 (1995) |
| j30 | | B. John Oommen, Hassan Masum: Switching models for nonstationary random environments. IEEE Transactions on Systems, Man, and Cybernetics 25(9): 1334-1339 (1995) |
| c13 | | B. John Oommen, R. K. S. Loke: Noisy Subsequence Recognition Using Constrained String Editing Involving Substitutions, Insertions, Deletions and Generalized Transpositions. ICSC 1995: 116-123 |
| c12 | | B. John Oommen, Edward V. de St. Croix: On Using Learning Automata for Fast Graph Partitioning. LATIN 1995: 449-460 |
| 1994 | ||
| j29 | | B. John Oommen, David T. H. Ng: A New Technique for Enhancing Linked-List Data Retrieval: Reorganize Data Using Artificially synthesized Queries. Comput. J. 37(7): 598-609 (1994) |
| j28 | | |
| 1993 | ||
| j27 | | B. John Oommen: Transforming Ill-Conditioned Constrained Problems using Projections. Comput. J. 36(3): 282-285 (1993) |
| j26 | | B. John Oommen, Chris Fothergill: Fast Learning Automaton-Based Image Examination and Retrieval. Comput. J. 36(6): 542-553 (1993) |
| j25 | | Radhakrishna S. Valiveti, B. John Oommen: Self-Organizing Doubly-Linked Lists. J. Algorithms 14(1): 88-114 (1993) |
| j24 | | B. John Oommen, Jack R. Zgierski: Breaking Substitution Cyphers Using Stochastic Automata. IEEE Trans. Pattern Anal. Mach. Intell. 15(2): 185-192 (1993) |
| j23 | | Radhakrishna S. Valiveti, B. John Oommen: Determining stochastic dependence for normally distributed vectors using the chi-squared metric. Pattern Recognition 26(6): 975-987 (1993) |
| j22 | | B. John Oommen, David T. H. Ng: An Optimal Absorbing List Organization Strategy with Constant Memory Requirements. Theor. Comput. Sci. 119(2): 355-361 (1993) |
| j21 | | Robert P. Cheetham, B. John Oommen, David T. H. Ng: Adaptive Structuring of Binary Search Trees Using Conditional Rotations. IEEE Trans. Knowl. Data Eng. 5(4): 695-704 (1993) |
| j20 | | David T. H. Ng, B. John Oommen, E. R. Hansen: Adaptive learning mechanisms for ordering actions using random races. IEEE Transactions on Systems, Man, and Cybernetics 23(5): 1450-1465 (1993) |
| 1992 | ||
| j19 | | David T. H. Ng, B. John Oommen: A Short Note on Doubly-Linked List Reorganizing Heuristics. Comput. J. 35(5): 533-535 (1992) |
| j18 | | B. John Oommen, I. Reichstein: On the problem of multiple mobile robots cluttering a workspace. Inf. Sci. 63(1-2): 55-85 (1992) |
| j17 | | Radhakrishna S. Valiveti, B. John Oommen: On using the chi-squared metric for determining stochastic dependence. Pattern Recognition 25(11): 1389-1400 (1992) |
| j16 | | J. Kevin Lanctôt, B. John Oommen: Discretized estimator learning automata. IEEE Transactions on Systems, Man, and Cybernetics 22(6): 1473-1483 (1992) |
| 1991 | ||
| j15 | | Radhakrishna S. Valiveti, B. John Oommen: Recognizing Sources of Random Strings. IEEE Trans. Pattern Anal. Mach. Intell. 13(4): 386-394 (1991) |
| j14 | | B. John Oommen, Radhakrishna S. Valiveti, Jack R. Zgierski: An adaptive learning solution to the keyboard optimization problem. IEEE Transactions on Systems, Man, and Cybernetics 21(6): 1608-1618 (1991) |
| c11 | | Radhakrishna S. Valiveti, B. John Oommen, Jack R. Zgierski: Adaptive Linear List Reorganization for a System Processing Set Queries. FCT 1991: 405-414 |
| 1990 | ||
| j13 | | B. John Oommen, David T. H. Ng: On Generating Random Permutations with Arbitrary Distributions. Comput. J. 33(4): 368-374 (1990) |
| j12 | | B. John Oommen, E. R. Hansen, J. Ian Munro: Deterministic Optimal and Expedient Move-to-Rear List Organizing Strategies. Theor. Comput. Sci. 74(2): 183-197 (1990) |
| c10 | | B. John Oommen, Radhakrishna S. Valiveti, Jack R. Zgierski: A Fast Learning Automaton Solution to the Keyboard Optimization Problem. IEA/AIE (Vol. 2) 1990: 981-990 |
| 1989 | ||
| c9 | | B. John Oommen, David T. H. Ng: On Generating Random Permutations with Arbitrary Distributions. ACM Conference on Computer Science 1989: 27-32 |
| c8 | | David T. H. Ng, B. John Oommen: Generalizing Singly-Linked List Reorganizing Heuristics for Doubly-Linked Lists. MFCS 1989: 380-389 |
| c7 | | B. John Oommen, David T. H. Ng: Optimal Constant Space Move-to-Rear List Organization. Optimal Algorithms 1989: 115-125 |
| 1988 | ||
| j11 | | B. John Oommen: Correction to "Recognition of Noisy Subsequences Using Constrained Edit Distances". IEEE Trans. Pattern Anal. Mach. Intell. 10(6): 983-984 (1988) |
| j10 | | B. John Oommen, Daniel C. Y. Ma: Deterministic Learning Automata Solutions to the Equipartitioning Problem. IEEE Trans. Computers 37(1): 2-13 (1988) |
| c6 | | Robert P. Cheetham, B. John Oommen, David T. H. Ng: On Using Conditional Rotation Operations to Adaptively Structure Binary Search Trees. ICDT 1988: 161-175 |
| 1987 | ||
| j9 | | B. John Oommen: Recognition of Noisy Subsequences Using Constrained Edit Distances. IEEE Trans. Pattern Anal. Mach. Intell. 9(5): 676-685 (1987) |
| j8 | | B. John Oommen, E. R. Hansen: List Organizing Strategies Using Stochastic Move-to-Front and Stochastic Move-to-Rear Operations. SIAM J. Comput. 16(4): 705-716 (1987) |
| c5 | | B. John Oommen, Daniel C. Y. Ma: Fast Object Partitioning Using Stochastic Learning Automata. SIGIR 1987: 111-122 |
| 1986 | ||
| j7 | | |
| c4 | | B. John Oommen, S. Sitharama Iyengar, Nageswara S. V. Rao, Rangasami L. Kashyap: Robot Navigation in Unknown Terrains of Convex Polygonal Obstacles Using Learned Visibility Graphs. AAAI 1986: 1101-1106 |
| c3 | | B. John Oommen, E. R. Hansen: Expedient Stochastic Move-to-Front and optimal Move-to-Rear List Organizing Strategies. ICDT 1986: 349-364 |
| 1985 | ||
| j6 | | B. John Oommen, M. A. L. Thathachar: Multiaction learning automata possessing ergodicity of the mean. Inf. Sci. 35(3): 183-198 (1985) |
| c2 | | B. John Oommen: On the Futility of Arbitrarily Increasing Memory Capabilities of Stochastic Learning Automata. CAIA 1985: 308-312 |
| 1984 | ||
| c1 | | B. John Oommen: Algorithms for String Editing which Permit Arbitrarily Complex Editing Constraints. MFCS 1984: 443-451 |
| 1983 | ||
| j5 | | Rangasami L. Kashyap, B. John Oommen: Scale Preserving Smoothing of Polygons. IEEE Trans. Pattern Anal. Mach. Intell. 5(6): 667-671 (1983) |
| j4 | | Rangasami L. Kashyap, B. John Oommen: The Noisy Substring Matching Problem. IEEE Trans. Software Eng. 9(3): 365-370 (1983) |
| 1982 | ||
| j3 | | Rangasami L. Kashyap, B. John Oommen: A Geometrical Approach to Polygonal Dissimilarity and Shape Matching. IEEE Trans. Pattern Anal. Mach. Intell. 4(6): 649-654 (1982) |
| 1981 | ||
| j2 | | Rangasami L. Kashyap, B. John Oommen: An effective algorithm for string correction using generalized edit distances--I. Description of the algorithm and its optimality. Inf. Sci. 23(2): 123-142 (1981) |
| j1 | | Rangasami L. Kashyap, B. John Oommen: An effective algorithm for string correction using generalized edit distance - II. Computational complexity of the algorithm and some applications. Inf. Sci. 23(3): 201-217 (1981) |
Colors in the list of coauthors
Last update Thu May 23 19:35:40 2013 CET by the DBLP Team —
Data released under the ODC-BY 1.0 license — See also our legal information page