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| [1] |
Paul Bassan, Joe Mellor, Jonathan Shapiro, Kaye J. Williams, Michael Lisant
iand, and Peter Gardner.
Transmission ftir chemical imaging on glass substrates: applications
in infrared spectral histopathology.
Analytical Chemistry, 2014. [ bib | http ] |
| [2] |
Joseph Mellor and Jonathan Shapiro.
Thompson sampling in switching environments with bayesian online
change detection.
In AISTATS [3], pages 442-450. [ bib | html ] |
| [3] |
Proceedings of the Sixteenth International Conference on Artificial
Intelligence and Statistics, AISTATS 2013, Scottsdale, AZ, USA, April 29 -
May 1, 2013, volume 31 of JMLR Proceedings. JMLR.org, 2013. [ bib | http ] |
| [4] |
Richard Mealing and Jonathan L. Shapiro.
Opponent modelling by sequence prediction and lookahead in two-player
games.
In Rutkowski et al. [5], pages 385-396. [ bib | http ] |
| [5] |
Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz,
Lotfi A. Zadeh, and Jacek M. Zurada, editors.
Artificial Intelligence and Soft Computing - 12th International
Conference, ICAISC 2013, Zakopane, Poland, June 9-13, 2013, Proceedings, Part
II, volume 7895 of Lecture Notes in Computer Science. Springer, 2013. [ bib | http ] |
| [6] |
Piotr Kowalczyk, Paul Glendinning, Martin Brown, Gustavo Medrano-Cerda, Houman
Dallali, and Jonathan Shapiro.
Modelling human balance using switched systems with linear feedback
control.
Journal of The Royal Society Interface, 9(67):234-245, 2012. [ bib ] |
| [7] |
James BT Sanders, Tobias Galla, and Jonathan L Shapiro.
Effects of noise on convergent game-learning dynamics.
Journal of Physics A: Mathematical and Theoretical,
45(10):105001, 2012. [ bib ] |
| [8] |
Markus Schläpfer and Jonathan L. Shapiro.
Modeling failure propagation in large-scale engineering networks.
Complex Science, Lecture Notes of the Institute for Computer
Sciences, Social Informatics, and Telecommunications Engineering, 5:2127 -
2138, 2009.
Also presented at the <a href=http://complex-sys.org/2009/>First
International Conference, Complex 2009</a>, Shanghai, China, February 23-25,
2009. Revised Papers, Part 2. [ bib | http ] |
| [9] |
John M. Butterworth and Jonathan L. Shapiro.
Stability of learning dynamics in two-agent, imperfect information
games.
In Proceedings of the tenth ACM SIGEVO workshop on Foundations
of genetic algorithms. ACM, 2009. [ bib ] |
| [10] |
John M Butterworth and Jonathan L Shapiro.
Stability of learning dynamics in two-agent, imperfect-information
games.
In Proceedings of the tenth ACM SIGEVO workshop on Foundations
of genetic algorithms, pages 131-140. ACM, 2009. [ bib ] |
| [11] |
Markus Schläpfer and Jonathan L Shapiro.
Modeling failure propagation in large-scale engineering networks.
In Complex Sciences, pages 2127-2138. Springer, 2009. [ bib ] |
| [12] |
Simon M. Lucas and Thomas P. Runarsson, editors.
Opponent Modelling in Heads-Up Poker, 2008. [ bib | .html ] |
| [13] |
Jürgen Branke, Clemens Lode, and Jonathan L. Shapiro.
Addressing sampling errors and diversity loss in umda.
In Hod Lipson, editor, Genetic and Evolutionary Computation
Conference, GECCO 2007, Proceedings, London, England, UK, July 7-11, 2007,
pages 508-515. ACM, 2007. [ bib | http ] |
| [14] |
Chong Liu and Jonathan L. Shapiro.
Implementing classical conditioning with spiking neurons.
Lecture Notes in Computer Science, 4668:400-410, 2007. [ bib ] |
| [15] |
Hao Wu and Jonathan L. Shapiro.
Parameter cross-validation and early-stopping in univariate marginal
distribution algorithm.
In Hod Lipson, editor, Genetic and Evolutionary Computation
Conference, GECCO 2007, Proceedings, London, England, UK, July 7-11, 2007,
pages 632-633. ACM, 2007. [ bib | http ] |
| [16] |
Stephen B. Furber, Gavin Brown, Joy Bose, John Michael Cumpstey, Peter
Marshall, and Jonathan L. Shapiro.
Sparse distributed memory using rank-order neural codes.
IEEE Transactions on Neural Networks, 18(3):648-659, 2007. [ bib | http ] |
| [17] |
Elon S. Correa and Jonathan L. Shapiro.
Model complexity vs. performance in the bayesian optimization
algorithm.
Lecture Notes in Computer Science, 4193:998-1007, 2006. [ bib ] |
| [18] |
J. Bose, S. B. Furber, and J. L. Shapiro.
A system for transmitting a coherent burst of activity through a
network of spiking neurons.
Lecture Notes in Computer Science, 3931:44-48, 2006. [ bib ] |
| [19] |
Jonathan L. Shapiro.
Diversity loss in general estimation of distibution algorithms.
Lecture Notes in Computer Science, 4193:92-101, 2006. [ bib ] |
| [20] |
Hao Wu and Jonathan L. Shapiro.
Does overfitting affect performance in estimation of distribution
algorithms.
In Proceedings of Genetic and Evolutionary Computation
Conference (GECCO) 2006, Seattle, 2006. [ bib ] |
| [21] |
Hao Wu and Jonathan L. Shapiro.
Choosing search algorithms in bayesian optimization algorithm.
In Ardil [22], pages 51-55. [ bib ] |
| [22] |
Cemal Ardil, editor.
International Enformatika Conference, IEC'05, August 26-28,
2005, Prague, Czech Republic, CDROM. Enformatika, Canakkale, Turkey,
2005. [ bib ] |
| [23] |
J. Bose, S. B. Furber, and J. L. Shapiro.
A spiking neural sparse distributed memory implementation for
learning and predicting temporal sequences.
Lecture Notes in Computer Science, 3696:115-120, 2005. [ bib ] |
| [24] |
J. Bose, S. B. Furber, and J. L. Shapiro.
An associative memory for the on-line recognition and prediction of
temporal sequences.
In Proceedings of International Joint Conference on Neural
Networks 2005, Montreal, 2005. IEEE. [ bib ] |
| [25] |
Stephen Marsland, Ulrich Nehmzow, and Jonathan Shapiro.
On-line novelty detection for autonomous mobile robots.
Robotics and Autonomous Systems, 51(2-3):191-206, 2005. [ bib | .pdf ] |
| [26] |
J. L. Shapiro.
Drift and scaling in estimation of distribution algorithms.
Evolutionary Computation, 13(1):99-125, 2005. [ bib ] |
| [27] |
Jason Fleischer and Jonathan L. Shapiro.
Imitation is not enough for lexicon learning.
In Stefan Schaal, Jean-Arcady Meyer, John Hallam, Aude Billard, and
Auke Jan Ijspeert, editors, From Animals to Animats 8: Proceedings of
the Eighth International Conference on the Simulation of Adaptive Behavior,
pages 477-486. International Society for Adaptive Behaviour, Bradford Books,
2004.
ISBN: 0262693410. [ bib ] |
| [28] |
Sybil Hirsch, Timothy L Frank, Jonathan L Shapiro, Michelle L Hazell, and
Peter I Frank.
Development of a questionnaire weighted scoring system to target
diagnostic examinations for asthma in adults: a modelling study.
BMC Family Practice, 5(30), 2004. [ bib | http ] |
| [29] |
J. L. Shapiro.
Scaling of probability-based optimization algorithms.
Advances in Neural Information Processing, 15, 2003. [ bib ] |
| [30] |
Jason Fleischer, Stephen Marsland, and Jonathan Shapiro.
Sensory anticipation for autonomous selection of robot landmarks.
Lecture Notes in Artificial Intelligence, 2684:201-221, 2003. [ bib ] |
| [31] |
Sybil Hirsch, J. L. Shapiro, and Peter I. Frank.
The application of neural network system techniques to asthma
screening and prevalence estimation.
In Cornelius t. Leondes, editor, Handbook of Computational
Methods in Biomaterials, Biotechnology, and Biomedical Systems, pages
141-160. Kluwer, 2003. [ bib ] |
| [32] |
E. S. Correa and J. L. Shapiro.
A study of the effect of detailed balance on PBIL in k-sat and
the p-median problem.
In International Conference on Advances in Soft Computing,
2002. [ bib ] |
| [33] |
J. L. Shapiro.
The sensitivity of PBIL to its learning rate, and how detailed
balance can remove it.
In Carlos Cotta, Kenneth de Jong, Riccardo Poli, and Jonathan Rowe,
editors, Foundations of Genetic Algorithms VII. Morgan Kaufmann, 2002. [ bib ] |
| [34] |
Tom Duckett, Stephen Marsland, and Jonathan Shapiro.
Fast, on-line learning of globally consistent maps.
Autonomous Robots, 12(3):287-300, 2002. [ bib ] |
| [35] |
Stephen Marsland, Jonathan Shapiro, and Ulrich Nehmzow.
A self-organising network that grows when required.
Neural Networks, 15(8-9):1041-1058, 2002. [ bib ] |
| [36] |
Stephen Marsland, Ulrich Nehmzow, and Jonathan Shapiro.
Environment-specific novelty detection.
In From Animals to Animats, the 7th International Conference on
Simulation of Adaptive Behaviour, Edinburgh, 2002. [ bib ] |
| [37] |
J. L. Shapiro and John Wearden.
Reinforcement learning and time perception - a model of animal
experiments.
Advances in Neural Information Processing 14, 2002. [ bib ] |
| [38] |
Tom Duckett, Stephen Marsland, and Jonathan Shapiro.
Simultaneous localization and mapping - a new algorithm for a
compass-equipped mobile robot.
In Proceedings of the IJCAI-2001 Workshop on Reasoning with
Uncertainty in Robotics, Seattle, Washington, 2001. [ bib ] |
| [39] |
Sybil Hirsch, Jonathan Shapiro, Michael Turega, Timothy L. Frank, Robert Niven,
and Peter I. Frank.
Using a neural network to screen a population for asthma.
Annals of Epidemiology, 11:369 - 376, 2001. [ bib ] |
| [40] |
A. Johnson and J. L. Shapiro.
The importance of selection mechanisms in distribution estimation
algorithms.
In Proceedings of the 5th International Conference
on Artificial Evolution AE01, 2001. [ bib | .ps.gz ] |
| [41] |
Jonathan Shapiro.
Genetic algorithms in machine learning.
In G. Paliouras, V. Karkaletsis, and C. D. Spyropoulos, editors,
Machine Learning and Its Applications, volume LNAI 2049, pages 146 - 169.
Springer, 2001. [ bib ] |
| [42] |
Ulrich Nehmzow Stephen Marsland and Jonathan Shapiro.
Novelty detection in large environments.
In Proceedings of Towards Intelligent Mobile Robots 2001
(TIMR'01), 2001. [ bib ] |
| [43] |
Stephen Marsland, Ulrich Nehmzow, and Jonathan Shapiro.
Vision-based environmental novelty detection on a mobile robot.
In Proceedings of the International Conference on Neural
Information Processing (ICONIP'01), Shanghai, 2001. [ bib ] |
| [44] |
Magnus Rattray and Jonathan L. Shapiro.
Cumulative dynamics of a population under multiplicative selection,
mutation, and drift.
Theoretical Population Biology, 60:17 - 32, 2001. [ bib ] |
| [45] |
J. L. Shapiro, John Wearden, and Rossano Barone.
A simple model exhibiting scalar timing.
In Robert M. French and Jacques P. Sougnè, editors,
Connectionist Models of Learning, Development, and Evolution, Perspectives
in Neural Computing. Springer, 2001. [ bib ] |
| [46] |
J. L. Shapiro.
Statistical mechanics theory of genetic algorithms.
In L. Kallel, B. Naudts, and A. Rogers, editors, Theoretical
Aspects of Evolutionary Computing, pages 87 - 108. Springer, 2001. [ bib ] |
| [47] |
Tom Duckett, Stephen Marsland, and Jonathan Shapiro.
Learning globally consistent maps by relaxation.
In Proceedings of the International Conference on Robotics and
Automation (ICRA'2000), pages 3841 - 3846, 2000. [ bib | .ps.Z ] |
| [48] |
Stephen Marsland, Ulrich Nehmzow, and Jonathan Shapiro.
Novelty detection for robot neotaxis.
In Proceedings of the 2nd International Symposium on Neural
Computation (NC'2000), pages 554 - 559, 2000. [ bib | .ps.Z ] |
| [49] |
Stephen Marsland, Ulrich Nehmzow, and Jonathan Shapiro.
A real-time novelty detector for a mobile robot.
In Proceedings of the EUREL Conference on Advanced Robotics
Systems, 2000. [ bib | .ps.Z ] |
| [50] |
Stephen Marsland, Ulrich Nehmzow, and Jonathan Shapiro.
Novelty detection on a mobile robot using habituation.
In From Animals to Animats: The 6th International Conference on
Simulation of Adaptive Behaviour, 2000. [ bib | .ps.Z ] |
| [51] |
Stephen Marsland, Ulrich Nehmzow, and Jonathan Shapiro.
A model of habituation applied to mobile robots.
In Proceedings of TIMR 99, Towards Intelligent Mobile Robots,
Bristol. Department of Computer Science, Manchester University, Technical
Report Series, ISSN 1361-6161, Report UMCS-99-3-1, 1999. [ bib | .ps.Z ] |
| [52] |
J. L. Shapiro.
Does data-model co-evolution improve generalization performance of
evolving learner?
Lecture Notes in Computer Science, 1498:540 - 549, 1998. [ bib ] |
| [53] |
J. L. Shapiro and A. Prügel-Bennett.
Genetic algorithms dynamics in two-well potentials with basins and
barriers.
In R. K. Belew and M. D. Vose, editors, Foundations of Genetic
Algorithms 4, pages 102 - 116. Morgan Kaufmann, 1997. [ bib ] |
| [54] |
Jonathan Shapiro, Adam Prügel-Bennett, and Magnus Rattray.
A statistical mechanics analysis of genetic algorithms for search and
learning.
In S. W. Ellacott, J. C. Mason, and I. J. Anderson, editors,
Mathematics of Neural Networks: Models, Algorithms and Applications, pages
318 - 323, 1997. [ bib ] |
| [55] |
Jonathan Foster and Jonathan Shapiro.
Hippocampal and related structures.
In M. Conway, editor, Cognitive Models of Memory. Psychology
Press, 1997. [ bib ] |
| [56] |
Sybil Hirsch, Jonathan Shapiro, and Peter Frank.
Use of an artificial neural network in estimating prevalence and
assessing underdiagnosis of asthma.
Neural Computing and Applications, 5:124 - 128, 1997. [ bib ] |
| [57] |
David Corne and Jonathan L. Shapiro, editors.
Evolutionary Computing, volume 1305 of Lecture Notes in
Computer Science.
Springer, 1997. [ bib ] |
| [58] |
M. Rattray and J. L. Shapiro.
Noisy fitness evaluations in genetic algorithms and the dynamics of
learning.
In R. K. Belew and M. D. Vose, editors, Foundations of Genetic
Algorithms 4, pages 117 - 139. Morgan Kaufmann, 1997. [ bib ] |
| [59] |
A. Prügel-Bennett and J. L. Shapiro.
The dynamics of a genetic algorithm for simple Ising systems.
Physica D, 104:75-114, 1997. [ bib ] |
| [60] |
Jonathan Shapiro, Magnus Rattray, and Adam Prügel-Bennett.
Maximum entropy analysis of genetic algorithms.
In K. M. Hanson and R. N. Silver, editors, Maximum Entropy and
Bayesian Methods, pages 303 - 310, 1996. [ bib ] |
| [61] |
M. Rattray and J. Shapiro.
The dynamics of genetic algorithms for a simple learning problem.
Journal of Physics: A, 29:7451 - 7473, 1996. [ bib ] |
| [62] |
M. Malloch, J. L. Shapiro, G. Hitch, V. Culpin, and J. Towse.
Temporal effects in immediate verbal memory: A combined experimental
modelling approach.
Language and Cognitive Processes, 10(3-4):401 - 405, 1995. [ bib ] |
| [63] |
Jonathan L. Shapiro and Adam Prügel-Bennett.
Maximum entropy analysis of genetic algorithm operators.
Lecture Notes in Computer Science, 993:14-24, 1995. [ bib ] |
| [64] |
Jonathan Shapiro and Adam Prügel-Bennett.
Non-linear statistical analysis and self-organizing Hebbian
networks'.
Advances in Neural Information Processing, 6:407 - 414, 1994. [ bib ] |
| [65] |
A. Prügel-Bennett and J. L. Shapiro.
An analysis of genetic algorithms using statistical mechanics.
Physical Review Letters, 72(9):1305 - 1309, 1994. [ bib | http ] |
| [66] |
Jonathan Shapiro, Adam Prügel-Bennett, and Magnus Rattray.
A statistical mechanical formulation of the dynamics of genetic
algorithms'.
Lecture Notes in Computer Science, 865:17 - 27, 1994. [ bib ] |
| [67] |
Jonathan Shapiro and Adam Prügel-Bennett.
Unsupervised Hebbian learning,.
In J. Bower and F. Eeckmann, editors, Computation and Neural
Systems, pages 25 - 30. Kluwer Academic Publishers, 1993. [ bib ] |
| [68] |
Adam Prügel-Bennett and Jonathan Shapiro.
Statistical mechanics of unsupervised Hebbian learning.
Journal of Physics A, 26:2343 - 2396, 1993. [ bib ] |
| [69] |
Adam Prügel-Bennett and Jonathan Shapiro.
The partitioning problem in unsupervised learning for non-linear
neurons.
Journal of Physics A, 26:7417 - 7426, 1993. [ bib ] |
| [70] |
Jonathan Shapiro and Adam Prügel-Bennett.
Unsupervised learning and the shape of the neuron activation
function.
In I. Aleksander and J. Taylor, editors, Artificial Neural
Networks II, pages 179 - 182. Elsevier Science Publishers, 1992. [ bib ] |
| [71] |
Jonathan Shapiro, Hojung Cha, and Ron Daniels Jr.
Parallel machine simulation for the design of architectures for
neural networks.
In K. Boyanov, editor, Proceedings of the 3rd Workshop on
Parallel and Distributed Processing, pages 323 - 332. Elsvier Scientific
Publisher, 1992. [ bib ] |
| [72] |
Neil Burgess, J. L. Shapiro, and M. A. Moore.
Neural network models of list learning.
Network: Computation in Neural Systems, 4(2):399 - 422, 1991. [ bib ] |
| [73] |
Neil Burgess, M. A. Moore, and J. L. Shapiro.
Human-like forgetting in neural network models of memory.
In W. K. Theumann and R. Koberle, editors, Neural Networks and
Spin Glasses. World Scientific,, 1990. [ bib ] |
| [74] |
J. L. Shapiro.
Hard learning in boolean neural networks.
In J.G. Taylor, editor, Neural Computing. IOP Publishing LTD,
1989. [ bib ] |
| [75] |
J. L. Shapiro.
Hard learning in boolean neural networks.
In J. G. Taylor, editor, Neural Computing. IOP Publishing LTD,
1989. [ bib ] |
| [76] |
J. L. Shapiro.
A solvable model of hard learning.
In L. Personnaz and G. Dreyfus, editors, Neural Networks, From
Models to Applications. IDSET, 1988. [ bib ] |
| [77] |
J. L. Shapiro.
Phase transitions in finite systems.
Physical Review Letters, 56:2225, 1986. [ bib ] |
| [78] |
J. L. Shapiro and J. Rudnick.
The fully finite spherical model.
Journal of Statistical Physics, 43(1-2):51 - 83, 1985. [ bib | http ] |