dblp.uni-trier.dewww.uni-trier.de

B. John Oommen Vis

List of publications from the DBLP Bibliography Server - FAQ
Coauthor Index - Ask others: ACM DL/Guide - CiteSeerX - CSB - MetaPress - Google - Bing - Yahoo
Home Page

*2009
174EEOle-Christoffer Granmo, B. John Oommen: A Hierarchy of Twofold Resource Allocation Automata Supporting Optimal Sampling. IEA/AIE 2009: 523-534
173EEB. John Oommen, M. Khaled Hashem: Learning Automata Based Intelligent Tutorial-like System. KES (1) 2009: 360-373
172EEEser Aygün, B. John Oommen, Zehra Cataltepe: On Utilizing Optimal and Information Theoretic Syntactic Modeling for Peptide Classification. PRIB 2009: 24-35
171EEB. John Oommen, Sudip Misra: Cybernetics and Learning Automata. Handbook of Automation 2009: 221-235
170EESudip 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)
169EESang-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)
2008
168EEB. John Oommen, Ebaa Fayyoumi: Enhancing Micro-Aggregation Technique by Utilizing Dependence-Based Information in Secure Statistical Databases. ACISP 2008: 404-418
167EEB. John Oommen, Ebaa Fayyoumi: An AI-Based Causal Strategy for Securing Statistical Databases Using Micro-aggregation. Australasian Conference on Artificial Intelligence 2008: 423-434
166EELuis Rueda, Claudio Henríquez, B. John Oommen: Chernoff-Based Multi-class Pairwise Linear Dimensionality Reduction. CIARP 2008: 301-308
165EESang-Woon Kim, B. John Oommen: A Fast Computation of Inter-class Overlap Measures Using Prototype Reduction Schemes. Canadian Conference on AI 2008: 173-184
164EEOle-Christoffer Granmo, B. John Oommen: A Hierarchy of Twofold Resource Allocation Automata Supporting Optimal Web Polling. IEA/AIE 2008: 347-358
163EEKe Qin, B. John Oommen: Chaotic Pattern Recognition: The Spectrum of Properties of the Adachi Neural Network. SSPR/SPR 2008: 540-550
162EEB. John Oommen, Dragos Calitoiu: Modeling and simulating a disease outbreak by learning a contagion parameter-based model. SpringSim 2008: 547-555
161EEDragos Calitoiu, Doron Nussbaum, B. John Oommen: Large scale modeling of the piriform cortex for analyzing antiepileptic effects. SpringSim 2008: 599-608
160EEDragos Calitoiu, B. John Oommen, Doron Nussbaum: Spikes annihilation in the Hodgkin-Huxley neuron. Biological Cybernetics 98(3): 239-257 (2008)
159EEB. 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)
158EESang-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)
157EELuis 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)
2007
156EESudip Misra, B. John Oommen: The Pursuit Automaton Approach for Estimating All-Pairs Shortest Paths in Dynamically Changing Networks. AINA Workshops (1) 2007: 124-129
155EEDragos 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
154EEOle-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
153EEDragos Calitoiu, B. John Oommen, Doron Nussbaum: Numerical Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation. BVAI 2007: 378-387
152EEDragos Calitoiu, B. John Oommen, Doron Nussbaum: Analytic Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation. Canadian Conference on AI 2007: 320-331
151EEB. John Oommen, Ebaa Fayyoumi: A Novel Method for Micro-Aggregation in Secure Statistical Databases Using Association and Interaction. ICICS 2007: 126-140
150EEM. 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
149EEB. 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
148EEM. Khaled Hashem, B. John Oommen: Using learning automata to model a student-classroom interaction in a tutorial-like system. SMC 2007: 1177-1182
147EEM. Khaled Hashem, B. John Oommen: Using learning automata to model the behavior of a teacher in a tutorial-like system. SMC 2007: 76-82
146EEAbdelrahman 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)
145EEB. 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)
144EEOle-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)
143EEDragos 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)
142EEB. John Oommen, Ghada Hany Badr: Breadth-first search strategies for trie-based syntactic pattern recognition. Pattern Anal. Appl. 10(1): 1-13 (2007)
141EEDragos 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)
140EESang-Woon Kim, B. John Oommen: On using prototype reduction schemes to optimize dissimilarity-based classification. Pattern Recognition 40(11): 2946-2957 (2007)
139EEB. 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)
138EEPradeep K. Atrey, Mohan S. Kankanhalli, B. John Oommen: Goal-oriented optimal subset selection of correlated multimedia streams. TOMCCAP 3(1): (2007)
2006
137EEEbaa Fayyoumi, B. John Oommen: On Optimizing the k-Ward Micro-aggregation Technique for Secure Statistical Databases. ACISP 2006: 324-335
136EEXavier Hilaire, B. John Oommen: The averaged mappings problem: statement, applications, and approximate solution. ACM Southeast Regional Conference 2006: 24-29
135 Denis V. Batalov, B. John Oommen: Turning Lights Out with DQ-Learning. Artificial Intelligence and Applications 2006: 451-456
134EEB. 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
133 B. John Oommen, Jing Chen: On Utilizing Attribute Cardinality Maps to Enhance Query Optimization in the Oracle Database System. ICEIS (1) 2006: 23-35
132EEB. John Oommen, Jing Chen: On Enhancing Query Optimization in the Oracle Database System by Utilizing Attribute Cardinality Maps. ICEIS (Selected Papers) 2006: 38-71
131EESang-Woon Kim, B. John Oommen: On Optimizing Dissimilarity-Based Classification Using Prototype Reduction Schemes. ICIAR (1) 2006: 15-28
130EEB. John Oommen, Sudip Misra, Ole-Christoffer Granmo: A Stochastic Random-Races Algorithm for Routing in MPLS Traffic Engineering. INFOCOM 2006
129EEEbaa Fayyoumi, B. John Oommen: A Fixed Structure Learning Automaton Micro-aggregation Technique for Secure Statistical Databases. Privacy in Statistical Databases 2006: 114-128
128EEB. 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
127EESang-Woon Kim, B. John Oommen: On Optimizing Kernel-Based Fisher Discriminant Analysis Using Prototype Reduction Schemes. SSPR/SPR 2006: 826-834
126EEAbdelrahman Amer, B. John Oommen: Lists on Lists: A Framework for Self-organizing Lists in Environments with Locality of Reference. WEA 2006: 109-120
125EESudip 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)
124EELuis Rueda, B. John Oommen: A fast and efficient nearly-optimal adaptive Fano coding scheme. Inf. Sci. 176(12): 1656-1683 (2006)
123EEGhada 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)
122EESang-Woon Kim, B. John Oommen: Prototype reduction schemes applicable for non-stationary data sets. Pattern Recognition 39(2): 209-222 (2006)
121EEB. 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)
2005
120EEGhada 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
119EESang-Woon Kim, B. John Oommen: Time-Varying Prototype Reduction Schemes Applicable for Non-stationary Data Sets. Australian Conference on Artificial Intelligence 2005: 614-623
118EEDragos Calitoiu, B. John Oommen, Doron Nussbaum: Neural Network-Based Chaotic Pattern Recognition - Part 2: Stability and Algorithmic Issues. CORES 2005: 3-16
117EEGhada Badr, B. John Oommen: A Look-Ahead Branch and Bound Pruning Scheme for Trie-Based Approximate String Matching. CORES 2005: 87-94
116EEGhada Badr, B. John Oommen: Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies. ICAPR (1) 2005: 1-17
115EEGeir Horn, B. John Oommen: A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem. IPDPS 2005
114EESudip Misra, B. John Oommen: New Algorithms for Maintaining All-Pairs Shortest Paths. ISCC 2005: 116-121
113EELuís G. Rueda, B. John Oommen: Efficient Adaptive Data Compression Using Fano Binary Search Trees. ISCIS 2005: 768-779
112EEB. 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
111EEDragos Calitoiu, B. John Oommen, Dorin Nusbaumm: Modeling Inaccurate Perception: Desynchronization Issues of a Chaotic Pattern Recognition Neural Network. SCIA 2005: 821-830
110EEB. John Oommen, Luís G. Rueda: A formal analysis of why heuristic functions work. Artif. Intell. 164(1-2): 1-22 (2005)
109EEGhada Hany Badr, B. John Oommen: Self-Adjusting of Ternary Search Tries Using Conditional Rotations and Randomized Heuristics. Comput. J. 48(2): 200-219 (2005)
108EESang-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)
107EESang-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)
106EESudip 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)
2004
105 Sudip Misra, B. John Oommen: Adaptive Algorithms for Routing and Traffic Engineering in Stochastic Networks. AAAI 2004: 993-994
104EEB. John Oommen, Jack R. Zgierski, Doron Nussbaum: Deterministic Majority filters applied to stochastic sorting. ACM Southeast Regional Conference 2004: 228-233
103EELuís G. Rueda, B. John Oommen: On Families of New Adaptive Compression Algorithms Suitable for Time-Varying Source Data. ADVIS 2004: 234-244
102EESang-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
101EESudip Misra, B. John Oommen: Stochastic Learning Automata-Based Dynamic Algorithms for the Single Source Shortest Path Problem. IEA/AIE 2004: 239-248
100EESudip Misra, B. John Oommen: Generalized pursuit learning algorithms for shortest path routing tree computation. ISCC 2004: 891-896
99 B. John Oommen, Jack R. Zgierski, Doron Nussbaum: Stochastic Sorting Using Deterministic Consecutive and Leader Filters. MSV/AMCS 2004: 399-405
98 Qun Wang, B. John Oommen: On Designing Pattern Classifiers Using Artificially Created Bootstrap Samples. PRIS 2004: 159-168
97 B. John Oommen: Recent Results on Learning from Stochastic Teachers and Compulsive Liars/Con-Men. PRIS 2004: 4
96EEB. John Oommen, Ghada Badr: Dictionary-Based Syntactic Pattern Recognition Using Tries. SSPR/SPR 2004: 251-259
95EEB. John Oommen, Luís G. Rueda: A New Family of Weak Estimators for Training in Non-stationary Distributions. SSPR/SPR 2004: 644-652
94EESang-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)
93EELuís G. Rueda, B. John Oommen: A nearly-optimal Fano-based coding algorithm. Inf. Process. Manage. 40(2): 257-268 (2004)
92EEM. 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)
91EESang-Woon Kim, B. John Oommen: On using prototype reduction schemes to optimize kernel-based nonlinear subspace methods. Pattern Recognition 37(2): 227-239 (2004)
2003
90EEB. 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
89EESang-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
88EEOuerd Messaouda, B. John Oommen, Stan Matwin: Enhancing Caching in Distributed Databases Using Intelligent Polytree Representations. Canadian Conference on AI 2003: 498-504
87EEB. John Oommen, Jing Chen: A new histogram method for sparse attributes: the averaged rectangular attribute cardinality map. ISICT 2003: 119-125
86 Qun Wang, B. John Oommen: Classification Error-Rate Estimation Using New Pseudo-Sample Bootstrap Methods. PRIS 2003: 96-103
85 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)
84EESang-Woon Kim, B. John Oommen: A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Anal. Appl. 6(3): 232-244 (2003)
83EELuís G. Rueda, B. John Oommen: On optimal pairwise linear classifiers for normal distributions: the d-dimensional case. Pattern Recognition 36(1): 13-23 (2003)
82EESang-Woon Kim, B. John Oommen: Enhancing prototype reduction schemes with LVQ3-type algorithms. Pattern Recognition 36(5): 1083-1093 (2003)
2002
81EESang-Woon Kim, B. John Oommen: Optimizing Kernel-Based Nonlinear Subspace Methods Using Prototype Reduction Schemes. Australian Joint Conference on Artificial Intelligence 2002: 155-166
80 Sang-Woon Kim, B. John Oommen: On Utilizing LVQ3-Type Algorithms to Enhance Prototype Reduction Schemes. PRIS 2002: 242-256
79 B. John Oommen, Luís G. Rueda: Using Pattern Recognition Techniques to Derive a Formal Analysis of Why Heuristics Functions Work. PRIS 2002: 45-58
78EESang-Woon Kim, B. John Oommen: Recursive Prototype Reduction Schemes Applicable for Large Data Sets. SSPR/SPR 2002: 528-537
77EEB. John Oommen, Luís G. Rueda: The Efficiency of Histogram-like Techniques for Database Query Optimization. Comput. J. 45(5): 494-510 (2002)
76EELuís 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)
75 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)
74 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)
73EEGopal 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)
2001
72EELuís G. Rueda, B. John Oommen: Resolving Minsky's Paradox: The d-Dimensional Normal Distribution Case. Australian Joint Conference on Artificial Intelligence 2001: 25-36
71 B. John Oommen, Luís G. Rueda: Histogram Methods in Query Optimization: The Relation between Accuracy and Optimality. DASFAA 2001: 320-326
70EEGopal 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
69 B. John Oommen, Qun Wang: Distance Bias Adjustment Bootstrap Estimation for Bhattacharyya Error Bound in Classifiers. PRIS 2001: 103-117
68EEB. 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)
67 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)
2000
66 Necati Aras, I. Kuban Altinel, B. John Oommen: A Kohonen-like Decomposition Method for the Traveling Salesman Problem: KNIESDECOMPOSE. ECAI 2000: 261-265
65 B. John Oommen, Luis Rueda: An Empirical Comparison of Histogram-Like Techniques for Query Optimization. ICEIS 2000: 71-78
64EEB. John Oommen, Murali Thiyagarajah: Query Result Size Estimation Using the Trapezoidal Attribute Cardinality Map. IDEAS 2000: 236-242
63EEM. Ouerd, B. John Oommen, Stan Matwin: A Formalism for Building Causal Polytree Structures Using Data Distributions. ISMIS 2000: 629-637
62EELuis Rueda, B. John Oommen: The Foundational Theory of Optimal Bayesian Pairwise Linear Classifiers. SSPR/SPR 2000: 581-590
61EEB. John Oommen, T. Dale Roberts: Continuous Learning Automata Solutions to the Capacity Assignment Problem. IEEE Trans. Computers 49(6): 608-620 (2000)
1999
60EEMurali Thiyagarajah, B. John Oommen: On Benchmarking Attribute Cardinality Maps for Database Systems Using the TPC-D Specification. DEXA 1999: 292-301
59 Murali Thiyagarajah, B. John Oommen: Prototype Validation of the Trapezoidal Attribute Cardinality Map for Query Optimization in Database Systems. ICEIS 1999: 156-162
58EEB. John Oommen, Murali Thiyagarajah: Query Result Size Estimation Using a Novel Histogram-like Technique: The Rectangular Attribute Cardinality Map. IDEAS 1999: 3-15
57 B. John Oommen, T. Dale Roberts: On Solving the Capacity Assignment Problem Using Continous Learning Automata. IEA/AIE 1999: 622-631
56 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)
55EENecati 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)
1998
54 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
53 B. John Oommen, R. K. S. Loke: The Noisy Subsequence Tree Recognition Problem. SSPR/SPR 1998: 169-180
52 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)
51 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)
50EEB. John Oommen, Rangasami L. Kashyap: A formal theory for optimal and information theoretic syntactic pattern recognition. Pattern Recognition 31(8): 1159-1177 (1998)
1997
49EEB. John Oommen, Juan Dong: Generalized Swap-with-Parent Schemes for Self-Organizing Sequential Linear Lists. ISAAC 1997: 414-423
48EEQingxin Zhu, B. John Oommen: On the Optimal Search Problem: The Case when the Target Distribution is Unknown. SCCC 1997: 268-277
47EEThai 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)
46 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)
45 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)
44EEI. Kuban Altinel, B. John Oommen, Necati Aras: Vector Quantization for Arbitrary Distance Function Estimation. INFORMS Journal on Computing 9(4): 439-451 (1997)
43EEB. John Oommen, Richard K. S. Loke: Pattern recognition of strings with substitutions, insertions, deletions and generalized transpositions. Pattern Recognition 30(5): 789-800 (1997)
1996
42EEB. John Oommen, R. K. S. Loke: Optimal and Information Theoretic Syntactic Pattern Recognition Involving Traditional and Transposition Errors. FSTTCS 1996: 224-237
41 B. John Oommen, Rangasami L. Kashyap: Optimal and Information Theoretic Syntactic Pattern Recognition for Traditional Errors. SSPR 1996: 11-20
40 B. John Oommen, K. Zhang, William Lee: Numerical Similarity and Dissimilarity Measures Between Two Trees. IEEE Trans. Computers 45(12): 1426-1434 (1996)
39 B. John Oommen, Edward V. de St. Croix: Graph Partitioning Using Learning Automata. IEEE Trans. Computers 45(2): 195-208 (1996)
38EEB. John Oommen, K. Zhang: The Normalized String Editing Problem Revisited. IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 669-672 (1996)
1995
37 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
36 B. John Oommen, Edward V. de St. Croix: On Using Learning Automata for Fast Graph Partitioning. LATIN 1995: 449-460
35 B. John Oommen: String Alignment with Substitution, Insertion, Deletion, Squashing and Expansion Operations. Inf. Sci. 83(1&2): 89-107 (1995)
1994
34 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)
33 B. John Oommen, William Lee: Constrained Tree Editing. Inf. Sci. 77(3-4): 253-273 (1994)
1993
32 B. John Oommen: Transforming Ill-Conditioned Constrained Problems using Projections. Comput. J. 36(3): 282-285 (1993)
31 B. John Oommen, Chris Fothergill: Fast Learning Automaton-Based Image Examination and Retrieval. Comput. J. 36(6): 542-553 (1993)
30EERobert 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)
29EEB. John Oommen, Jack R. Zgierski: Breaking Substitution Cyphers Using Stochastic Automata. IEEE Trans. Pattern Anal. Mach. Intell. 15(2): 185-192 (1993)
28 Radhakrishna S. Valiveti, B. John Oommen: Self-Organizing Doubly-Linked Lists. J. Algorithms 14(1): 88-114 (1993)
27EERadhakrishna S. Valiveti, B. John Oommen: Determining stochastic dependence for normally distributed vectors using the chi-squared metric. Pattern Recognition 26(6): 975-987 (1993)
26 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)
1992
25 David T. H. Ng, B. John Oommen: A Short Note on Doubly-Linked List Reorganizing Heuristics. Comput. J. 35(5): 533-535 (1992)
24EEB. John Oommen, I. Reichstein: On the problem of multiple mobile robots cluttering a workspace. Inf. Sci. 63(1-2): 55-85 (1992)
23EERadhakrishna S. Valiveti, B. John Oommen: On using the chi-squared metric for determining stochastic dependence. Pattern Recognition 25(11): 1389-1400 (1992)
1991
22 Radhakrishna S. Valiveti, B. John Oommen, Jack R. Zgierski: Adaptive Linear List Reorganization for a System Processing Set Queries. FCT 1991: 405-414
21EERadhakrishna S. Valiveti, B. John Oommen: Recognizing Sources of Random Strings. IEEE Trans. Pattern Anal. Mach. Intell. 13(4): 386-394 (1991)
1990
20EEB. 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
19 B. John Oommen, David T. H. Ng: On Generating Random Permutations with Arbitrary Distributions. Comput. J. 33(4): 368-374 (1990)
18 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)
1989
17 B. John Oommen, David T. H. Ng: On Generating Random Permutations with Arbitrary Distributions. ACM Conference on Computer Science 1989: 27-32
16EEDavid T. H. Ng, B. John Oommen: Generalizing Singly-Linked List Reorganizing Heuristics for Doubly-Linked Lists. MFCS 1989: 380-389
15EEB. John Oommen, David T. H. Ng: Optimal Constant Space Move-to-Rear List Organization. Optimal Algorithms 1989: 115-125
1988
14EERobert P. Cheetham, B. John Oommen, David T. H. Ng: On Using Conditional Rotation Operations to Adaptively Structure Binary Search Trees. ICDT 1988: 161-175
13 B. John Oommen, Daniel C. Y. Ma: Deterministic Learning Automata Solutions to the Equipartitioning Problem. IEEE Trans. Computers 37(1): 2-13 (1988)
12EEB. John Oommen: Correction to "Recognition of Noisy Subsequences Using Constrained Edit Distances". IEEE Trans. Pattern Anal. Mach. Intell. 10(6): 983-984 (1988)
1987
11EEB. John Oommen, Daniel C. Y. Ma: Fast Object Partitioning Using Stochastic Learning Automata. SIGIR 1987: 111-122
10 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)
1986
9 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
8EEB. John Oommen, E. R. Hansen: Expedient Stochastic Move-to-Front and optimal Move-to-Rear List Organizing Strategies. ICDT 1986: 349-364
7EEB. John Oommen: Constrained string editing. Inf. Sci. 40(3): 267-284 (1986)
1985
6 B. John Oommen: On the Futility of Arbitrarily Increasing Memory Capabilities of Stochastic Learning Automata. CAIA 1985: 308-312
5EEB. John Oommen, M. A. L. Thathachar: Multiaction learning automata possessing ergodicity of the mean. Inf. Sci. 35(3): 183-198 (1985)
1984
4EEB. John Oommen: Algorithms for String Editing which Permit Arbitrarily Complex Editing Constraints. MFCS 1984: 443-451
1983
3 Rangasami L. Kashyap, B. John Oommen: The Noisy Substring Matching Problem. IEEE Trans. Software Eng. 9(3): 365-370 (1983)
1981
2EERangasami 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)
1EERangasami 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)

Coauthor Index

1M. Agache [67] [75]
2I. Kuban Altinel [44] [52] [55] [66]
3Abdelrahman Amer [126] [146]
4Necati Aras [44] [52] [55] [66]
5Pradeep K. Atrey [138]
6Eser Aygün [172]
7Ghada Hany Badr (Ghada Badr) [96] [109] [116] [117] [120] [123] [142]
8Denis V. Batalov [135]
9Dragos Calitoiu [111] [118] [141] [143] [152] [153] [155] [160] [161] [162]
10Zehra Cataltepe [172]
11Robert P. Cheetham [14] [30]
12Jing Chen [87] [132] [133]
13Edward V. de St. Croix [36] [39] [46]
14Juan Dong [49]
15Ebaa Fayyoumi [129] [137] [151] [167] [168]
16Chris Fothergill [31]
17Ole-Christoffer Granmo [130] [134] [144] [145] [149] [154] [159] [164] [174]
18E. R. Hansen [8] [10] [18]
19M. Khaled Hashem [147] [148] [150] [173]
20Claudio Henríquez [166]
21Xavier Hilaire [136]
22Geir Horn [115] [128] [139]
23S. Sitharama Iyengar [9]
24Mohan S. Kankanhalli [138]
25Rangasami L. Kashyap [1] [2] [3] [9] [41] [50]
26Sang-Woon Kim [78] [80] [81] [82] [84] [89] [91] [94] [102] [107] [108] [119] [122] [127] [128] [131] [139] [140] [149] [158] [159] [165] [169]
27Benjamin Kuipers [90]
28William Lee [33] [40]
29Richard K. S. Loke (R. K. S. Loke) [37] [42] [43] [53] [56] [68]
30Daniel C. Y. Ma [11] [13]
31Stan Matwin [63] [88] [92]
32Ouerd Messaouda [88]
33Sudip Misra [100] [101] [105] [106] [114] [125] [130] [145] [156] [170] [171]
34J. Ian Munro [18]
35Svein Arild Myrer [144]
36David T. H. Ng [14] [15] [16] [17] [19] [25] [26] [30] [34]
37Thai B. Nguyen [47]
38Dorin Nusbaumm [111]
39Doron Nussbaum [99] [104] [118] [141] [143] [152] [153] [155] [160] [161]
40Morten Goodwin Olsen [144]
41M. Ouerd [63] [92]
42Asle Pedersen [134]
43Ke Qin [163]
44Gopal Racherla [70] [73]
45Sridhar Radhakrishnan [70] [73]
46Govindachari Raghunath [51] [90]
47Nageswara S. V. Rao [9]
48I. Reichstein [24]
49T. Dale Roberts [54] [57] [61] [74]
50Luis Rueda (Luís G. Rueda) [62] [65] [71] [72] [76] [77] [79] [83] [93] [95] [103] [110] [112] [113] [121] [124] [157] [166]
51M. T. Samuel [159]
52Mathew Samuel [149]
53M. A. L. Thathachar [5]
54Murali Thiyagarajah [58] [59] [60] [64] [85]
55Radhakrishna S. Valiveti [20] [21] [22] [23] [27] [28]
56Qun Wang [69] [86] [98]
57Jack R. Zgierski [20] [22] [29] [99] [104]
58K. Zhang [38] [40]
59Qingxin Zhu [48]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)