Jonathan L. Shapiro

Jonathan Shapiro

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35EEJürgen Branke, Clemens Lode, Jonathan L. Shapiro: Addressing sampling errors and diversity loss in UMDA. GECCO 2007: 508-515
34EEHao Wu, Jonathan L. Shapiro: Parameter cross-validation and early-stopping in univariate marginal distribution algorithm. GECCO 2007: 632-633
33EEChong Liu, Jonathan Shapiro: Implementing Classical Conditioning with Spiking Neurons. ICANN (1) 2007: 400-410
32EEStephen 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)
31EEHao Wu, Jonathan L. Shapiro: Does overfitting affect performance in estimation of distribution algorithms. GECCO 2006: 433-434
30EEJonathan Shapiro: Programming language challenges in systems codes: why systems programmers still use C, and what to do about it. PLOS 2006: 9
29EEJonathan L. Shapiro: Diversity Loss in General Estimation of Distribution Algorithms. PPSN 2006: 92-101
28EEElon S. Correa, Jonathan L. Shapiro: Model Complexity vs. Performance in the Bayesian Optimization Algorithm. PPSN 2006: 998-1007
27EEJoy 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)
26EEJoy 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
25 Hao Wu, Jonathan L. Shapiro: Choosing Search Algorithms in Bayesian Optimization Algorithm. IEC (Prague) 2005: 51-55
24EEJoy 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
23EEJonathan L. Shapiro: Drift and Scaling in Estimation of Distribution Algorithms. Evolutionary Computation 13(1): 99-123 (2005)
22EEStephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: On-line novelty detection for autonomous mobile robots. Robotics and Autonomous Systems 51(2-3): 191-206 (2005)
21EEJason Fleischer, Stephen Marsland, Jonathan Shapiro: Sensory Anticipation for Autonomous Selection of Robot Landmarks. Anticipatory Behavior in Adaptive Learning Systems 2003: 201-221
20EEJonathan L. Shapiro: Scaling of Probability-Based Optimization Algorithms. NIPS 2002: 383-390
19 Tom Duckett, Stephen Marsland, Jonathan Shapiro: Fast, On-Line Learning of Globally Consistent Maps. Auton. Robots 12(3): 287-300 (2002)
18EEStephen Marsland, Jonathan Shapiro, Ulrich Nehmzow: A self-organising network that grows when required. Neural Networks 15(8-9): 1041-1058 (2002)
17EEAndrew Johnson, Jonathan L. Shapiro: The Importance of Selection Mechanisms in Distribution Estimation Algorithms. Artificial Evolution 2001: 91-103
16EEJonathan L. Shapiro: Genetic Algorithms in Machine Learning. Machine Learning and Its Applications 2001: 146-168
15EEJonathan L. Shapiro, J. Wearden: Reinforcement Learning and Time Perception -- a Model of Animal Experiments. NIPS 2001: 115-122
14 Tom Duckett, Stephen Marsland, Jonathan Shapiro: Learning Globally Consistent Maps by Relaxation. ICRA 2000: 3841-3846
13EEStephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: Novelty Detection for Robot Neotaxis CoRR cs.RO/0006005: (2000)
12EEStephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: A Real-Time Novelty Detector for a Mobile Robot CoRR cs.RO/0006006: (2000)
11EEStephen Marsland, Ulrich Nehmzow, Jonathan Shapiro: Novelty Detection on a Mobile Robot Using Habituation CoRR cs.RO/0006007: (2000)
10EEJonathan L. Shapiro: Does Data-Model Co-evolution Improve Generalization Performance of Evolving Learners? PPSN 1998: 540-549
9 David Corne, Jonathan L. Shapiro: Evolutionary Computing, AISB International Workshop, Manchester, UK, April 7-8, 1997, Selected Papers Springer 1997
8 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)
7 Jonathan Shapiro, Adam Prügel-Bennett: Genetic Algorithm Dynamics in a Two-well Potential. FOGA 1996: 101-116
6 Magnus Rattray, Jonathan Shapiro: Noisy Fitness Evaluation in Genetic Algorithms and the Dynamics of Learning. FOGA 1996: 117-139
5 Jonathan L. Shapiro, Adam Prügel-Bennett: Maximum Entropy Analysis of Genetic Algorithm Operators. Evolutionary Computing, AISB Workshop 1995: 14-24
4 Jonathan Shapiro, Adam Prügel-Bennett, Magnus Rattray: A Statistical Mechanical Formulation of the Dynamics of Genetic Algorithms. Evolutionary Computing, AISB Workshop 1994: 17-27
3EEJonathan L. Shapiro, Adam Prügel-Bennett: Non-Linear Statistical Analysis and Self-Organizing Hebbian Networks. NIPS 1993: 407-414
2EEJonathan Shapiro, Peter Mowforth: Data Fusion in 3D Through Surface Tracking. IEA/AIE (Vol. 1) 1990: 163-168
1EEJonathan Shapiro, Jin Zhengping: An Interactive Colour Line Recognition System for Seismic Section Digitisation. MVA 1990: 223-226

Coauthor Index

1Joy Bose [24] [26] [27] [32]
2Jürgen Branke [35]
3G. Brown [32]
4David W. Corne (David Corne) [9]
5Elon S. Correa [28]
6J. Mike Cumpstey [32]
7Tom Duckett [14] [19]
8Jason Fleischer [21]
9Peter I. Frank [8]
10Stephen B. Furber (Steve Furber) [24] [26] [27] [32]
11Sybil Hirsch [8]
12Andrew Johnson [17]
13Chong Liu [33]
14Clemens Lode [35]
15P. Marshall [32]
16Stephen Marsland [11] [12] [13] [14] [18] [19] [21] [22]
17Peter Mowforth [2]
18Ulrich Nehmzow [11] [12] [13] [18] [22]
19Adam Prügel-Bennett [3] [4] [5] [7]
20Magnus Rattray [4] [6]
21J. Wearden [15]
22Hao Wu [25] [31] [34]
23Jin Zhengping [1]

Colors in the list of coauthors

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)