Please note: This is a beta version of the new dblp website.
You can find the classic dblp view of this page here.
You can find the classic dblp view of this page here.
Jonathan L. Shapiro
Jonathan Shapiro
2010 – today
- 2013
[c28]Richard Mealing, Jonathan L. Shapiro: Opponent Modelling by Sequence Prediction and Lookahead in Two-Player Games. ICAISC (2) 2013: 385-396- [i6]Joseph Mellor, Jonathan Shapiro: Thompson Sampling in Switching Environments with Bayesian Online Change Point Detection. CoRR abs/1302.3721 (2013)
- 2012
- [i5]Ruefei He, Jonathan Shapiro: Bayesian Mixture Models for Frequent Itemset Discovery. CoRR abs/1209.6001 (2012)
2000 – 2009
- 2009
- [c27]Markus Schläpfer, Jonathan L. Shapiro: Modeling Failure Propagation in Large-Scale Engineering Networks. Complex (2) 2009: 2127-2138
- [c26]John M. Butterworth, Jonathan L. Shapiro: Stability of learning dynamics in two-agent, imperfect-information games. FOGA 2009: 131-140
- 2007
- [j6]Stephen B. Furber, G. Brown, Joy Bose, J. Mike Cumpstey, P. Marshall, Jonathan L. Shapiro: Sparse Distributed Memory Using Rank-Order Neural Codes. IEEE Transactions on Neural Networks 18(3): 648-659 (2007)
- [c25]Jürgen Branke, Clemens Lode, Jonathan L. Shapiro: Addressing sampling errors and diversity loss in UMDA. GECCO 2007: 508-515
- [c24]Hao Wu, Jonathan L. Shapiro: Parameter cross-validation and early-stopping in univariate marginal distribution algorithm. GECCO 2007: 632-633
- [c23]Chong Liu, Jonathan Shapiro: Implementing Classical Conditioning with Spiking Neurons. ICANN (1) 2007: 400-410
- 2006
- [c22]Jonathan Shapiro: Programming language challenges in systems codes: why systems programmers still use C, and what to do about it. PLOS 2006: 9
- [c21]Hao Wu, Jonathan L. Shapiro: Does overfitting affect performance in estimation of distribution algorithms. GECCO 2006: 433-434
- [c20]Jonathan L. Shapiro: Diversity Loss in General Estimation of Distribution Algorithms. PPSN 2006: 92-101
- [c19]Elon Santos Correa, Jonathan L. Shapiro: Model Complexity vs. Performance in the Bayesian Optimization Algorithm. PPSN 2006: 998-1007
- [i4]Joy Bose, Stephen B. Furber, Jonathan L. Shapiro: An associative memory for the on-line recognition and prediction of temporal sequences. CoRR abs/cs/0611020 (2006)
- 2005
- [j5]Jonathan L. Shapiro: Drift and Scaling in Estimation of Distribution Algorithms. Evolutionary Computation 13(1): 99-123 (2005)
- [j4]Stephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: On-line novelty detection for autonomous mobile robots. Robotics and Autonomous Systems 51(2-3): 191-206 (2005)
- [c18]Joy Bose, Stephen B. Furber, Jonathan L. Shapiro: A Spiking Neural Sparse Distributed Memory Implementation for Learning and Predicting Temporal Sequences. ICANN (1) 2005: 115-120
- [c17]Hao Wu, Jonathan L. Shapiro: Choosing Search Algorithms in Bayesian Optimization Algorithm. IEC (Prague) 2005: 51-55
- [c16]Joy Bose, Stephen B. Furber, Jonathan L. Shapiro: A System for Transmitting a Coherent Burst of Activity Through a Network of Spiking Neurons. WIRN/NAIS 2005: 44-48
- 2003
- [c15]Jason Fleischer, Stephen Marsland, Jonathan Shapiro: Sensory Anticipation for Autonomous Selection of Robot Landmarks. ABiALS 2003: 201-221
- 2002
- [j3]Tom Duckett, Stephen Marsland, Jonathan Shapiro: Fast, On-Line Learning of Globally Consistent Maps. Auton. Robots 12(3): 287-300 (2002)
- [j2]Stephen Marsland, Jonathan Shapiro, Ulrich Nehmzow: A self-organising network that grows when required. Neural Networks 15(8-9): 1041-1058 (2002)
- [c14]Jonathan L. Shapiro: The Sensitivity of PBIL to Its Learning Rate, and How Detailed Balance Can Remove It. FOGA 2002: 115-132
- [c13]
- 2001
- [c12]Jonathan L. Shapiro: Genetic Algorithms in Machine Learning. Machine Learning and Its Applications 2001: 146-168
- [c11]Andrew Johnson, Jonathan L. Shapiro: The Importance of Selection Mechanisms in Distribution Estimation Algorithms. Artificial Evolution 2001: 91-103
- [c10]Jonathan L. Shapiro, J. Wearden: Reinforcement Learning and Time Perception -- a Model of Animal Experiments. NIPS 2001: 115-122
- 2000
- [c9]Tom Duckett, Stephen Marsland, Jonathan Shapiro: Learning Globally Consistent Maps by Relaxation. ICRA 2000: 3841-3846
- [i3]Stephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: Novelty Detection for Robot Neotaxis. CoRR cs.RO/0006005 (2000)
- [i2]Stephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: A Real-Time Novelty Detector for a Mobile Robot. CoRR cs.RO/0006006 (2000)
- [i1]Stephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: Novelty Detection on a Mobile Robot Using Habituation. CoRR cs.RO/0006007 (2000)
1990 – 1999
- 1998
- [c8]Jonathan L. Shapiro: Does Data-Model Co-evolution Improve Generalization Performance of Evolving Learners? PPSN 1998: 540-549
- 1997
- [j1]Sybil Hirsch, Jonathan L. Shapiro, Peter I. Frank: Use of an Artificial Neural Network in Estimating Prevalence and Assessing Underdiagnisis of Asthma. Neural Computing and Applications 5(2): 124-128 (1997)
- [e1]David Corne, Jonathan L. Shapiro (Eds.): Evolutionary Computing, AISB International Workshop, Manchester, UK, April 7-8, 1997, Selected Papers. Lecture Notes in Computer Science 1305, Springer 1997, ISBN 3-540-63476-2
- 1996
- [c7]Jonathan Shapiro, Adam Prügel-Bennett: Genetic Algorithm Dynamics in a Two-well Potential. FOGA 1996: 101-116
- [c6]Magnus Rattray, Jonathan Shapiro: Noisy Fitness Evaluation in Genetic Algorithms and the Dynamics of Learning. FOGA 1996: 117-139
- 1995
- [c5]Jonathan L. Shapiro, Adam Prügel-Bennett: Maximum Entropy Analysis of Genetic Algorithm Operators. Evolutionary Computing, AISB Workshop 1995: 14-24
- 1994
- [c4]Jonathan Shapiro, Adam Prügel-Bennett, Magnus Rattray: A Statistical Mechanical Formulation of the Dynamics of Genetic Algorithms. Evolutionary Computing, AISB Workshop 1994: 17-27
- 1993
- [c3]Jonathan L. Shapiro, Adam Prügel-Bennett: Non-Linear Statistical Analysis and Self-Organizing Hebbian Networks. NIPS 1993: 407-414
- 1990
- [c2]Jonathan Shapiro, Peter Mowforth: Data Fusion in 3D Through Surface Tracking. IEA/AIE (Vol. 1) 1990: 163-168
- [c1]Jonathan Shapiro, Zhengping Jin: An Interactive Colour Line Recognition System for Seismic Section Digitisation. MVA 1990: 223-226
Coauthor Index
data released under the ODC-BY 1.0 license. See also our legal information page
last updated on 2013-06-09 19:27 CEST by the dblp team