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Jonathan Lawry
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2020 – today
- 2024
- [c52]Nawid Keshtmand, Raúl Santos-Rodríguez, Jonathan Lawry:
Typicality-based point OOD detection with contrastive learning. NLDL 2024: 120-129 - [c51]Zachary R. Madin
, Jonathan Lawry
, Edmund R. Hunt
:
Collective Anomaly Perception During Multi-Robot Patrol: Constrained Interactions Can Promote Accurate Consensus. SAC 2024: 630-637 - 2023
- [i20]Nawid Keshtmand, Raúl Santos-Rodríguez, Jonathan Lawry:
Two-step counterfactual generation for OOD examples. CoRR abs/2302.05196 (2023) - [i19]Tom Bewley, Jonathan Lawry, Arthur Richards:
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback. CoRR abs/2305.16924 (2023) - [i18]Michael Crosscombe, Jonathan Lawry:
The Benefits of Interaction Constraints in Distributed Autonomous Systems. CoRR abs/2306.01179 (2023) - [i17]Zachary R. Madin, Jonathan Lawry, Edmund R. Hunt:
Collective Anomaly Perception During Multi-Robot Patrol: Constrained Interactions Can Promote Accurate Consensus. CoRR abs/2312.12639 (2023) - 2022
- [c50]Michael Crosscombe, Jonathan Lawry:
The Benefits of Interaction Constraints in Distributed Autonomous Systems. DARS 2022: 14-27 - [c49]Nawid Keshtmand, Raúl Santos-Rodríguez, Jonathan Lawry:
Understanding the Properties and Limitations of Contrastive Learning for Out-of-Distribution Detection. ICPR Workshops (1) 2022: 330-343 - [i16]Tom Bewley, Jonathan Lawry, Arthur Richards:
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction. CoRR abs/2201.07749 (2022) - [i15]Tom Bewley, Jonathan Lawry, Arthur Richards, Rachel Craddock, Ian Henderson:
Reward Learning with Trees: Methods and Evaluation. CoRR abs/2210.01007 (2022) - [i14]Nawid Keshtmand, Raúl Santos-Rodríguez, Jonathan Lawry:
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection. CoRR abs/2211.03183 (2022) - 2021
- [j53]Alice Haynes, Jonathan Lawry, Christopher Kent
, Jonathan Rossiter
:
FeelMusic: Enriching Our Emotive Experience of Music through Audio-Tactile Mappings. Multimodal Technol. Interact. 5(6): 29 (2021) - [j52]Chanelle Lee
, Jonathan Lawry, Alan F. T. Winfield
:
Negative updating applied to the best-of-n problem with noisy qualities. Swarm Intell. 15(1-2): 111-143 (2021) - [j51]Michael Crosscombe
, Jonathan Lawry
:
Collective preference learning in the best-of-n problem. Swarm Intell. 15(1-2): 145-170 (2021) - [c48]Tom Bewley, Jonathan Lawry:
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments. AAAI 2021: 11415-11422 - [c47]Michael Crosscombe, Jonathan Lawry:
The Impact of Network Connectivity on Collective Learning. DARS 2021: 82-94 - [c46]Zixuan Liu, Michael Crosscombe
, Jonathan Lawry:
Imprecise Fusion Operators for Collective Learning. ALIFE 2021: 77 - [c45]Chris Bennett, Seth Bullock, Jonathan Lawry:
Demonstrating the Differential Impact of Flock Heterogeneity on Multi-agent Herding. TAROS 2021: 147-157 - [i13]Michael Crosscombe, Jonathan Lawry:
The Impact of Network Connectivity on Collective Learning. CoRR abs/2106.00655 (2021) - 2020
- [j50]Jonathan Lawry, Chanelle Lee
:
Probability pooling for dependent agents in collective learning. Artif. Intell. 288: 103371 (2020) - [j49]Nouf Bindris
, Nello Cristianini, Jonathan Lawry:
Claim Consistency Checking Using Soft Logic. Mach. Learn. Knowl. Extr. 2(3): 147-171 (2020) - [c44]Tom Bewley
, Jonathan Lawry
, Arthur Richards
:
Modelling Agent Policies with Interpretable Imitation Learning. TAILOR 2020: 180-186 - [i12]Jonathan Lawry, Michael Crosscombe, David Harvey:
Distributed Possibilistic Learning in Multi-Agent Systems. CoRR abs/2001.07145 (2020) - [i11]Tom Bewley, Jonathan Lawry, Arthur Richards:
Modelling Agent Policies with Interpretable Imitation Learning. CoRR abs/2006.11309 (2020) - [i10]Tom Bewley
, Jonathan Lawry:
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments. CoRR abs/2009.04743 (2020)
2010 – 2019
- 2019
- [c43]Michael Crosscombe, Jonathan Lawry:
Evidence Propagation and Consensus Formation in Noisy Environments. AAMAS 2019: 1904-1906 - [c42]Jonathan Lawry, Michael Crosscombe
, David Harvey:
Epistemic Sets Applied to Best-of-n Problems. ECSQARU 2019: 301-312 - [c41]Jonathan Lawry, Van-Nam Huynh, Chris A. McMahon:
Three Valued Representation of Opinions in Affective Design. IUKM 2019: 109-121 - [c40]Michael Crosscombe
, Jonathan Lawry
, Palina Bartashevich
:
Evidence Propagation and Consensus Formation in Noisy Environments. SUM 2019: 310-323 - [i9]Michael Crosscombe, Jonathan Lawry:
Evidence Propagation and Consensus Formation in Noisy Environments. CoRR abs/1905.04840 (2019) - 2018
- [c39]Chanelle Lee, Jonathan Lawry, Alan F. T. Winfield:
Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics. ANTS Conference 2018: 97-108 - [c38]Chanelle Lee, Jonathan Lawry, Alan F. T. Winfield:
Combining Opinion Pooling and Evidential Updating for Multi-Agent Consensus. IJCAI 2018: 347-353 - [c37]Iván Palomares
, Michael Crosscombe
, Zhen-Song Chen, Jonathan Lawry:
Dual Consensus Measure for Multi-perspective Multi-criteria Group Decision Making. SMC 2018: 3313-3318 - 2017
- [c36]Jeremy Morse, Dejanira Araiza-Illan, Kerstin Eder, Jonathan Lawry, Arthur Richards
:
A fuzzy approach to qualification in design exploration for autonomous robots and systems. FUZZ-IEEE 2017: 1-6 - [c35]Michael Crosscombe
, Jonathan Lawry, Sabine Hauert, Martin E. Homer
:
Robust distributed decision-making in robot swarms: Exploiting a third truth state. IROS 2017: 4326-4332 - 2016
- [j48]Michael Crosscombe
, Jonathan Lawry:
A model of multi-agent consensus for vague and uncertain beliefs. Adapt. Behav. 24(4): 249-260 (2016) - [j47]Martha Lewis
, Jonathan Lawry:
Hierarchical conceptual spaces for concept combination. Artif. Intell. 237: 204-227 (2016) - [j46]Oliver Turnbull, Jonathan Lawry, Mark H. Lowenberg
, Arthur Richards
:
A cloned linguistic decision tree controller for real-time path planning in hostile environments. Fuzzy Sets Syst. 293: 1-29 (2016) - [j45]Jonathan Lawry:
Borderlines and probabilities of borderlines: On the interconnection between vagueness and uncertainty. J. Appl. Log. 14: 113-138 (2016) - [i8]Martha Anne Flinders Lewis, Jonathan Lawry:
Concept Generation in Language Evolution. CoRR abs/1601.06732 (2016) - [i7]Martha Anne Flinders Lewis, Jonathan Lawry:
A Label Semantics Approach to Linguistic Hedges. CoRR abs/1601.06738 (2016) - [i6]Martha Anne Flinders Lewis, Jonathan Lawry:
The Utility of Hedged Assertions in the Emergence of Shared Categorical Labels. CoRR abs/1601.06755 (2016) - [i5]Martha Lewis, Jonathan Lawry:
Emerging Dimension Weights in a Conceptual Spaces Model of Concept Combination. CoRR abs/1601.06763 (2016) - [i4]Jeremy Morse, Dejanira Araiza-Illan, Jonathan Lawry, Kerstin Eder:
Towards the Specification of Adaptive Robotic Systems. CoRR abs/1603.01082 (2016) - [i3]Jeremy Morse, Dejanira Araiza-Illan, Jonathan Lawry, Arthur Richards, Kerstin Eder:
A formal approach to analysing requirements conformance in adaptive systems. CoRR abs/1606.01077 (2016) - [i2]Michael Crosscombe, Jonathan Lawry:
Exploiting Vagueness for Multi-Agent Consensus. CoRR abs/1607.05540 (2016) - [i1]Michael Crosscombe, Jonathan Lawry:
A Model of Multi-Agent Consensus for Vague and Uncertain Beliefs. CoRR abs/1612.03433 (2016) - 2015
- [j44]Yongchuan Tang
, Jonathan Lawry:
On truth-gaps, truth-gluts, and bipolar propositions. Int. J. Approx. Reason. 56: 137-151 (2015) - [j43]Henrietta Eyre, Jonathan Lawry:
A Dempster-Shafer model of imprecise assertion strategies. J. Appl. Log. 13(4): 458-479 (2015) - 2014
- [j42]Henrietta Eyre, Jonathan Lawry:
Language games with vague categories and negations. Adapt. Behav. 22(5): 289-303 (2014) - [j41]Martha Anne Flinders Lewis
, Jonathan Lawry:
A label semantics approach to linguistic hedges. Int. J. Approx. Reason. 55(5): 1147-1163 (2014) - [j40]Jonathan Lawry:
Probability, fuzziness and borderline cases. Int. J. Approx. Reason. 55(5): 1164-1184 (2014) - [j39]Davide Ciucci
, Didier Dubois, Jonathan Lawry:
Borderline vs. unknown: comparing three-valued representations of imperfect information. Int. J. Approx. Reason. 55(9): 1866-1889 (2014) - [j38]Hongmei He
, Jonathan Lawry:
The linguistic attribute hierarchy and its optimisation for classification. Soft Comput. 18(10): 1967-1984 (2014) - 2013
- [j37]Samantha Royston
, Jonathan Lawry, K. Horsburgh:
A linguistic decision tree approach to predicting storm surge. Fuzzy Sets Syst. 215: 90-111 (2013) - [c34]Jonathan Lawry:
Flexible Concepts Are Fuzzy Concepts. On Fuzziness (1) 2013: 353-358 - [c33]Jonathan Lawry, Trevor Martin:
Conditional Beliefs in a Bipolar Framework. ECSQARU 2013: 364-375 - [c32]Martha Anne Flinders Lewis, Jonathan Lawry:
Concept Generation in Language Evolution. IJCAI 2013: 3229-3230 - [c31]Guanyi Li, Jonathan Lawry:
A Random Set and Rule-Based Regression Model Incorporating Image Labels. IUKM 2013: 101-113 - 2012
- [j36]Jonathan Lawry, Yongchuan Tang:
On truth-gaps, bipolar belief and the assertability of vague propositions. Artif. Intell. 191-192: 20-41 (2012) - [j35]Van-Nam Huynh, Jonathan Lawry, Yoshiteru Nakamori:
Integrated uncertainty management for decision making. Ann. Oper. Res. 195(1): 1-4 (2012) - [j34]Yongchuan Tang, Jonathan Lawry:
Information cells and information cell mixture models for concept modelling. Ann. Oper. Res. 195(1): 311-323 (2012) - [j33]Yongchuan Tang, Jonathan Lawry:
A bipolar model of vague concepts based on random set and prototype theory. Int. J. Approx. Reason. 53(6): 867-879 (2012) - [c30]Guanyi Li, Jonathan Lawry:
Possibilistic Rules from Fuzzy Prototypes. IPMU (2) 2012: 255-264 - [c29]Jonathan Lawry, Didier Dubois:
A Bipolar Framework for Combining Beliefs about Vague Propositions. KR 2012 - 2011
- [j32]Zengchang Qin, Jonathan Lawry:
Prediction and query evaluation using linguistic decision trees. Appl. Soft Comput. 11(5): 3916-3928 (2011) - [j31]Jonathan Lawry, Inés González Rodríguez
:
A bipolar model of assertability and belief. Int. J. Approx. Reason. 52(1): 76-91 (2011) - [c28]Yongchuan Tang, Jonathan Lawry:
Bipolar Semantic Cells: An Interval Model for Linguistic Labels. IUKM 2011: 60-71 - [e6]Yongchuan Tang, Van-Nam Huynh, Jonathan Lawry:
Integrated Uncertainty in Knowledge Modelling and Decision Making - International Symposium, IUKM 2011, Hangzhou, China, October 28-30, 2011. Proceedings. Lecture Notes in Computer Science 7027, Springer 2011, ISBN 978-3-642-24917-4 [contents] - 2010
- [j30]Jerry M. Mendel, Lotfi A. Zadeh, Enric Trillas, Ronald R. Yager, Jonathan Lawry, Hani Hagras
, Sergio Guadarrama:
What Computing with Words Means to Me [Discussion Forum]. IEEE Comput. Intell. Mag. 5(1): 20-26 (2010) - [j29]Yongchuan Tang, Jonathan Lawry:
A prototype-based rule inference system incorporating linear functions. Fuzzy Sets Syst. 161(21): 2831-2853 (2010) - [j28]Jerry M. Mendel, Jonathan Lawry, Lotfi A. Zadeh:
Foreword to the Special Section on Computing With Words. IEEE Trans. Fuzzy Syst. 18(3): 437-440 (2010) - [j27]Jonathan Lawry, Yongchuan Tang:
Granular Knowledge Representation and Inference Using Labels and Label Expressions. IEEE Trans. Fuzzy Syst. 18(3): 500-514 (2010) - [c27]Jonathan Lawry:
A Random Set and Prototype Theory Interpretation of Intuitionistic Fuzzy Sets. IPMU (1) 2010: 618-628 - [c26]Yongchuan Tang, Jonathan Lawry:
Information Cell Mixture Models: The Cognitive Representations of Vague Concepts. IUM 2010: 371-382 - [c25]Jonathan Lawry, Hongmei He
:
Linguistic Decision Trees for Fusing Tidal Surge Forecasting Models. SMPS 2010: 403-410 - [c24]Jonathan Lawry:
Imprecise Bipolar Belief Measures Based on Partial Knowledge from Agent Dialogues. SUM 2010: 205-218 - [e5]Van-Nam Huynh, Yoshiteru Nakamori, Jonathan Lawry, Masahiro Inuiguchi:
Integrated Uncertainty Management and Applications [revised papers from the International Symposium on Integrated Uncertainty Management and Applications, IUM 2010, Ishikawa, Japan, April 9-11, 2010]. Advances in Intelligent and Soft Computing 68, Springer 2010, ISBN 978-3-642-11959-0 [contents]
2000 – 2009
- 2009
- [j26]Jonathan Lawry, Yongchuan Tang:
Uncertainty modelling for vague concepts: A prototype theory approach. Artif. Intell. 173(18): 1539-1558 (2009) - [j25]Yongchuan Tang, Jonathan Lawry:
Linguistic modelling and information coarsening based on prototype theory and label semantics. Int. J. Approx. Reason. 50(8): 1177-1198 (2009) - [c23]Jonathan Lawry, Inés González Rodríguez
:
Generalised Label Semantics as a Model of Epistemic Vagueness. ECSQARU 2009: 626-637 - [c22]Hongmei He
, Jonathan Lawry:
A linguistic CMAC equivalent to a Linguistic Decision Tree for classification. IJCNN 2009: 1177-1183 - [p5]Jonathan Lawry, Yongchuan Tang:
A Random Set and Prototype Theory Model of Linguistic Query Evaluation. Scalable Fuzzy Algorithms for Data Management and Analysis 2009: 159-178 - [e4]Yongchuan Tang, Jonathan Lawry:
2009 Second International Symposium on Computational Intelligence and Design, ISCID 2009, Changsha, Hunan, China, 12-14 December 2009, 2 Volumes. IEEE Computer Society 2009, ISBN 978-0-7695-3865-5 [contents] - 2008
- [j24]Zengchang Qin, Jonathan Lawry:
LFOIL: Linguistic rule induction in the label semantics framework. Fuzzy Sets Syst. 159(4): 435-448 (2008) - [j23]Jonathan Lawry, Hongmei He
:
Multi-Attribute Decision Making Based on Label Semantics. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 16(Supplement-2): 69-86 (2008) - [j22]Jonathan Lawry:
Appropriateness measures: an uncertainty model for vague concepts. Synth. 161(2): 255-269 (2008) - [j21]Van-Nam Huynh
, Yoshiteru Nakamori, Jonathan Lawry:
A Probability-Based Approach to Comparison of Fuzzy Numbers and Applications to Target-Oriented Decision Making. IEEE Trans. Fuzzy Syst. 16(2): 371-387 (2008) - [j20]Nicholas J. Randon, Jonathan Lawry, K. Horsburgh, Ian D. Cluckie:
Fuzzy Bayesian Modeling of Sea-Level Along the East Coast of Britain. IEEE Trans. Fuzzy Syst. 16(3): 725-738 (2008) - [c21]Hongmei He
, Jonathan Lawry:
Linguistic attribute hierarchies for downwards propagation of information. FUZZ-IEEE 2008: 655-661 - [c20]Daniel R. McCulloch, Jonathan Lawry, Ian D. Cluckie:
Real-time flood forecasting using updateable linguistic decision trees. FUZZ-IEEE 2008: 1935-1942 - [c19]Jonathan Lawry, Yongchuan Tang:
Relating Prototype Theory and Label Semantics. SMPS 2008: 35-42 - [c18]Jonathan Lawry, Inés González Rodríguez
:
Non-parametric Density Estimation Based on Label Semantics. SMPS 2008: 183-189 - [p4]Jonathan Lawry:
Label Semantics as a Framework for Granular Modelling. Interval / Probabilistic Uncertainty and Non-Classical Logics 2008: 87-102 - [p3]Jonathan Lawry:
An Overview of Computing with Words using Label Semantics. Fuzzy Sets and Their Extensions: Representation, Aggregation and Models 2008: 65-87 - [p2]Zengchang Qin, Jonathan Lawry:
Knowledge Discovery in a Framework for Modelling with Words. Soft Computing for Knowledge Discovery and Data Mining 2008: 241-276 - [e3]Van-Nam Huynh, Yoshiteru Nakamori, Hiroakira Ono, Jonathan Lawry, Vladik Kreinovich, Hung T. Nguyen:
Interval / Probabilistic Uncertainty and Non-Classical Logics. Advances in Soft Computing 46, Springer 2008, ISBN 978-3-540-77663-5 [contents] - 2007
- [c17]Jonathan Lawry, Hongmei He
:
Linguistic Attribute Hierarchies for Multiple-Attribute Decision Making. FUZZ-IEEE 2007: 1-6 - [c16]Daniel R. McCulloch, Jonathan Lawry, Miguel A. Rico-Ramirez
, Ian D. Cluckie:
Classification of Weather Radar Images using Linguistic Decision Trees with Conditional Labelling. FUZZ-IEEE 2007: 1-6 - [c15]Zengchang Qin, Jonathan Lawry:
Fuzziness and Performance: An Empirical Study with Linguistic Decision Trees. IFSA (1) 2007: 407-416 - 2006
- [b1]Jonathan Lawry:
Modelling and Reasoning with Vague Concepts. Studies in Computational Intelligence 12, Springer 2006, ISBN 978-0-387-29056-0 - [j19]Nicholas J. Randon, Jonathan Lawry:
Classification and query evaluation using modelling with words. Inf. Sci. 176(4): 438-464 (2006) - [c14]Oliver Turnbull, Arthur Richards, Jonathan Lawry, Mark H. Lowenberg
:
Fuzzy Decision Tree Cloning of Flight Trajectory Optimisation for Rapid Path Planning. CDC 2006: 6361-6366 - [c13]Zengchang Qin, Jonathan Lawry:
High Level Fuzzy Labels for Vague Concepts. SMPS 2006: 317-324 - [c12]Viet Ha-Thuc, Quang-Anh Nguyen-Van, Tru Hoang Cao, Jonathan Lawry:
A Fuzzy Synset-Based Hidden Markov Model for Automatic Text Segmentation. SMPS 2006: 365-372 - [c11]Nicholas J. Randon, Jonathan Lawry, Ian D. Cluckie:
Online Learning for Fuzzy Bayesian Prediction. SMPS 2006: 405-412 - [e2]Jonathan Lawry, Enrique Miranda, Alberto Bugarín, Shoumei Li, María Angeles Gil, Przemyslaw Grzegorzewski, Olgierd Hryniewicz:
Soft Methods for Integrated Uncertainty Modelling, Proceedings of the 2006 International Workshop on Soft Methods in Probability and Statistics, SMPS 2006, Bristol, UK, 5-7 September 2006. Advances in Soft Computing 37, Springer 2006, ISBN 978-3-540-34776-7 [contents] - 2005
- [j18]Zengchang Qin, Jonathan Lawry:
Decision tree learning with fuzzy labels. Inf. Sci. 172(1-2): 91-129 (2005) - [c10]Zengchang Qin, Jonathan Lawry:
Hybrid Bayesian Estimation Trees Based on Label Semantics. ECSQARU 2005: 896-907 - [c9]Jonathan Lawry, Jim W. Hall, Guangtao Fu:
A Granular Semantics for Fuzzy Measures and its Application to Climate Change Scenarios. ISIPTA 2005: 223-221 - 2004
- [j17]Jonathan Lawry:
A framework for linguistic modelling. Artif. Intell. 155(1-2): 1-39 (2004) - [j16]Jonathan Lawry, Jim W. Hall
, R. Bovey:
Fusion of expert and learnt knowledge in a framework of fuzzy labels. Int. J. Approx. Reason. 36(2): 151-198 (2004) - [j15]Inés González Rodríguez
, Jonathan Lawry, James F. Baldwin:
Induction And Fusion Of Fuzzy Prototypes. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 12(4): 409-446 (2004) - [j14]Jim W. Hall
, Jonathan Lawry:
Generation, combination and extension of random set approximations to coherent lower and upper probabilities. Reliab. Eng. Syst. Saf. 85(1-3): 89-101 (2004) - 2003
- [j13]Nicholas J. Randon, Jonathan Lawry:
Probabilistic and fuzzy methods for information fusion in data mining. Int. J. Intell. Syst. 18(6): 609-631 (2003) - [j12]Jordi Recasens
, Jonathan Lawry:
Normalizing Possibility Distributions Using t-Norms. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 11(3): 343-360 (2003) - [c8]Jonathan Lawry, Jordi Recasens
:
A Random set Model for Fuzzy Labels. ECSQARU 2003: 357-369 - [c7]Nicholas J. Randon, Jonathan Lawry:
Fuzzy models for prediction based on random set semantics. EUSFLAT Conf. 2003: 659-664 - [c6]Inés González Rodríguez
, Jonathan Lawry, James F. Baldwin:
An Iterative Fuzzy Prototype Induction Algorithm. IWANN (1) 2003: 286-293 - [p1]Jonathan Lawry:
Random Sets and Appropriateness Degrees for Modelling with Labels. Modelling with Words 2003: 186-208 - [e1]Jonathan Lawry, James G. Shanahan, Anca L. Ralescu:
Modelling with Words - Learning, Fusion, and Reasoning within a Formal Linguistic Representation Framework. Lecture Notes in Computer Science 2873, Springer 2003, ISBN 3-540-20487-3 [contents] - 2001
- [j11]Jonathan Lawry:
A methodology for computing with words. Int. J. Approx. Reason. 28(2-3): 51-89 (2001) - [j10]Jonathan Lawry:
Possibilistic Normalisation and Reasoning Under Partial Inconsistency. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 9(4): 413-436 (2001) - [j9]Jonathan Lawry:
An Alternative Approach to Computing with Words. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 9(Supplement): 3-16 (2001) - [c5]Jonathan Lawry:
Label Semantics: A Formal Framework for Modeling with Words. ECSQARU 2001: 374-384 - [c4]Jonathan Lawry:
Query Evaluation from Linguistic Prototypes. FUZZ-IEEE 2001: 39-42 - [c3]Jim W. Hall, Jonathan Lawry:
Imprecise Probabilities of Engineering System Failure from Random and Fuzzy Set Reliability Analysis. ISIPTA 2001: 195-204 - 2000
- [j8]James F. Baldwin, Jonathan Lawry:
A New Approach to Learning Linguistic Control Rules. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 8(1): 21-44 (2000) - [c2]Jim F. Baldwin, Jonathan Lawry:
A c-fuzzy means algorithm for prototype induction. FUZZ-IEEE 2000: 164-169
1990 – 1999
- 1999
- [j7]Jim F. Baldwin, Jonathan Lawry, Trevor P. Martin:
A mass assignment method for prototype induction. Int. J. Intell. Syst. 14(10): 1041-1070 (1999) - 1998
- [j6]James F. Baldwin, Jonathan Lawry, Trevor P. Martin:
A note on the conditional probability of fuzzy subsets of a continuous domain. Fuzzy Sets Syst. 96(2): 211-222 (1998) - [j5]Jonathan Lawry:
A voting mechanism for fuzzy logic. Int. J. Approx. Reason. 19(3-4): 315-333 (1998) - [j4]James F. Baldwin, Jonathan Lawry, Trevor P. Martin:
The Application of Generalised Fuzzy Rules to Machine Learning and Automated Knowledge Discovery. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 6(5): 459-488 (1998) - 1997
- [j3]Jonathan Lawry:
An inductive method for inexact reasoning. Int. J. Approx. Reason. 16(2): 205-221 (1997) - [j2]James F. Baldwin, Jonathan Lawry, Trevor P. Martin:
A mass assignment based ID3 algorithm for decision tree induction. Int. J. Intell. Syst. 12(7): 523-552 (1997) - 1996
- [j1]Jim F. Baldwin, Jonathan Lawry, Trevor P. Martin:
A mass assignment theory of the probability of fuzzy events. Fuzzy Sets Syst. 83(3): 353-367 (1996) - 1992
- [c1]Jonathan Lawry, George M. Wilmers:
An Axiomatic Approach to Systems of Prior Distributions in Inexact Reasoning. Logic at Work 1992: 81-89
Coauthor Index
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