Christopher M. Bishop

According to our database, Christopher M. Bishop authored at least 58 papers between 1991 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Deep Learning - Foundations and Concepts

Christopher M. Bishop, Hugh Bishop

Springer, ISBN: 978-3-031-45467-7, 2024

2014

Students, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model.

Bar Shalem, Yoram Bachrach, John Guiver, Christopher M. Bishop

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Neural Networks.

Michael I. Jordan, Christopher M. Bishop

Proceedings of the Computing Handbook, 2014

2013

Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables.

Nevena Lazic, Christopher M. Bishop, John M. Winn

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2011

Embracing Uncertainty: Applied Machine Learning Comes of Age.

Christopher M. Bishop

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

2010

Embracing Uncertainty: The New Machine Intelligence.

Christopher M. Bishop

Proceedings of the Knowledge-Based and Intelligent Information and Engineering Systems, 2010

2009

A unified modeling approach to data-intensive healthcare.

Iain E. Buchan, John M. Winn, Christopher M. Bishop

Proceedings of the Fourth Paradigm: Data-Intensive Scientific Discovery, 2009

2008

A New Framework for Machine Learning.

Christopher M. Bishop

Proceedings of the Computational Intelligence: Research Frontiers, 2008

2007

<i>Pattern Recognition and Machine Learning</i>.

Christopher M. Bishop, Nasser M. Nasrabadi

J. Electronic Imaging, 2007

Pattern recognition and machine learning, 5th Edition.

Christopher M. Bishop

Information science and statistics, Springer, ISBN: 9780387310732, 2007

2006

Principled Hybrids of Generative and Discriminative Models.

Julia A. Lasserre, Christopher M. Bishop, Thomas P. Minka

Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

Comparison of Generative and Discriminative Techniques for Object Detection and Classification.

Ilkay Ulusoy, Christopher M. Bishop

Proceedings of the Toward Category-Level Object Recognition, 2006

2005

Variational Message Passing.

John M. Winn, Christopher M. Bishop

J. Mach. Learn. Res., 2005

Robust Bayesian mixture modelling.

Markus Svensén, Christopher M. Bishop

Neurocomputing, 2005

The 2005 PASCAL Visual Object Classes Challenge.

Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-Taylor, Amos J. Storkey, Sándor Szedmák, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang

Proceedings of the Machine Learning Challenges, 2005

Generative versus Discriminative Methods for Object Recognition.

Ilkay Ulusoy, Christopher M. Bishop

Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 2005

2004

Generative models and Bayesian model comparison for shape recognition.

Balaji Krishnapuram, Christopher M. Bishop, Martin Szummer

Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition, 2004

Distinguishing text from graphics in on-line handwritten ink.

Christopher M. Bishop, Markus Svensén, Geoffrey E. Hinton

Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition, 2004

Object Recognition via Local Patch Labelling.

Christopher M. Bishop, Ilkay Ulusoy

Proceedings of the Deterministic and Statistical Methods in Machine Learning, 2004

2003

Bayesian Hierarchical Mixtures of Experts.

Christopher M. Bishop, Markus Svensén

Proceedings of the UAI '03, 2003

Structured Variational Distributions in VIBES.

Christopher M. Bishop, John M. Winn

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

Super-resolution Enhancement of Video.

Christopher M. Bishop, Andrew Blake, Bhaskara Marthi

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002

Bayesian Image Super-Resolution.

Michael E. Tipping, Christopher M. Bishop

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

VIBES: A Variational Inference Engine for Bayesian Networks.

Christopher M. Bishop, David J. Spiegelhalter, John M. Winn

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

Feature representation and signal classification in fluorescence in-situ hybridization image analysis.

Boaz Lerner, William F. Clocksin, Seema Dhanjal, Maj A. Hultén, Christopher M. Bishop

IEEE Trans. Syst. Man Cybern. Part A, 2001

Optimising Synchronisation Times for Mobile Devices.

Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Probabilistic Modelling of Replica Divergence.

Antony I. T. Rowstron, Neil D. Lawrence, Christopher M. Bishop

Proceedings of HotOS-VIII: 8th Workshop on Hot Topics in Operating Systems, 2001

Hyperparameters for Soft Bayesian Model Selection.

Adrian Corduneanu, Christopher M. Bishop

Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

2000

Variational Relevance Vector Machines.

Christopher M. Bishop, Michael E. Tipping

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Non-linear Bayesian Image Modelling.

Christopher M. Bishop, John M. Winn

Proceedings of the Computer Vision - ECCV 2000, 6th European Conference on Computer Vision, Dublin, Ireland, June 26, 2000

1999

Mixtures of Probabilistic Principal Component Analysers.

Michael E. Tipping, Christopher M. Bishop

Neural Comput., 1999

Neural Network-Based Wind Vector Retrieval from Satellite Scatterometer Data.

Dan Cornford, Ian T. Nabney, Christopher M. Bishop

Neural Comput. Appl., 1999

1998

A Hierarchical Latent Variable Model for Data Visualization.

Christopher M. Bishop, Michael E. Tipping

IEEE Trans. Pattern Anal. Mach. Intell., 1998

GTM: The Generative Topographic Mapping.

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

Neural Comput., 1998

Developments of the generative topographic mapping.

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

Neurocomputing, 1998

Mixture Representations for Inference and Learning in Boltzmann Machines.

Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan

Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998

Bayesian PCA.

Christopher M. Bishop

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Latent Variable Models.

Christopher M. Bishop

Proceedings of the Learning in Graphical Models, 1998

1997

Bayesian Neural Networks.

Christopher M. Bishop

J. Braz. Comput. Soc., 1997

Regression with Input-dependent Noise: A Gaussian Process Treatment.

Paul W. Goldberg, Christopher K. I. Williams, Christopher M. Bishop

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Approximating Posterior Distributions in Belief Networks Using Mixtures.

Christopher M. Bishop, Neil D. Lawrence, Tommi S. Jaakkola, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Ensemble Learning for Multi-Layer Networks.

David Barber, Christopher M. Bishop

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Neural Networks.

Michael I. Jordan, Christopher M. Bishop

Proceedings of the Computer Science and Engineering Handbook, 1997

1996

Modeling Conditional Probability Distributions for Periodic Variables.

Christopher M. Bishop, Ian T. Nabney

Neural Comput., 1996

Neural Networks.

Michael I. Jordan, Christopher M. Bishop

ACM Comput. Surv., 1996

GTM: A Principled Alternative to the Self-Organizing Map.

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

Regression with Input-Dependent Noise: A Bayesian Treatment.

Christopher M. Bishop, Cazhaow S. Quazaz

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

Bayesian Model Comparison by Monte Carlo Chaining.

David Barber, Christopher M. Bishop

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

Bayesian Inference of Noise Levels in Regression.

Christopher M. Bishop, Cazhaow S. Quazaz

Proceedings of the Artificial Neural Networks, 1996

1995

Real-Time Control of a Tokamak Plasma Using Neural Networks.

Christopher M. Bishop, Paul S. Haynes, Mike E. U. Smith, Tom N. Todd, David L. Trotman

Neural Comput., 1995

Training with Noise is Equivalent to Tikhonov Regularization.

Christopher M. Bishop

Neural Comput., 1995

EM Optimization of Latent-Variables Density Models.

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

1994

Fast Feedback Control of a High Temperature Fusion Plasma.

Christopher M. Bishop, Paul S. Haynes, Mike E. U. Smith, Tom N. Todd, David L. Trotman

Neural Comput. Appl., 1994

Estimating Conditional Probability Densities for Periodic Variables.

Christopher M. Bishop, Claire Legleye

Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Real-Time Control of a Tokamak Plasma Using Neural Networks.

Christopher M. Bishop

Proceedings of the Advances in Neural Information Processing Systems 7, 1994

1993

Curvature-driven smoothing: a learning algorithm for feedforward networks.

Christopher M. Bishop

IEEE Trans. Neural Networks, 1993

Reconstruction of Tokamak Density Profiles Using Feedforward Networks.

Christopher M. Bishop, Iain Strachan, John O'Rourke, Geoff Maddison, Paul Thomas

Neural Comput. Appl., 1993

1991

A Fast Procedure for Retraining the Multilayer Perceptron.

Christopher M. Bishop

Int. J. Neural Syst., 1991