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.
Christopher M. Bishop
2010 – today
- 2013
[i3]Christopher M. Bishop, Michael E. Tipping: Variational Relevance Vector Machines. CoRR abs/1301.3838 (2013)- [i2]Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan: Mixture Representations for Inference and Learning in Boltzmann Machines. CoRR abs/1301.7393 (2013)
- 2012
- [i1]Christopher M. Bishop, Markus Svensén: Bayesian Hierarchical Mixtures of Experts. CoRR abs/1212.2447 (2012)
- 2011
- [c31]Christopher M. Bishop: Embracing Uncertainty: Applied Machine Learning Comes of Age. ECML/PKDD (1) 2011: 4
- 2010
- [c30]
2000 – 2009
- 2009
- [p2]Iain E. Buchan, John M. Winn, Christopher M. Bishop: A unified modeling approach to data-intensive healthcare. The Fourth Paradigm 2009: 91-97
- 2008
- [c29]
- 2007
- [j14]Christopher M. Bishop, Nasser M. Nasrabadi: Pattern Recognition and Machine Learning. J. Electronic Imaging 16(4): 049901 (2007)
- 2006
- [c28]Ilkay Ulusoy, Christopher M. Bishop: Comparison of Generative and Discriminative Techniques for Object Detection and Classification. Toward Category-Level Object Recognition 2006: 173-195
- [c27]Julia A. Lasserre, Christopher M. Bishop, Thomas P. Minka: Principled Hybrids of Generative and Discriminative Models. CVPR (1) 2006: 87-94
- 2005
- [j13]Markus Svensén, Christopher M. Bishop: Robust Bayesian mixture modelling. Neurocomputing 64: 235-252 (2005)
- [j12]John M. Winn, Christopher M. Bishop: Variational Message Passing. Journal of Machine Learning Research 6: 661-694 (2005)
- [c26]Ilkay Ulusoy, Christopher M. Bishop: Generative versus Discriminative Methods for Object Recognition. CVPR (2) 2005: 258-265
- [c25]Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc J. 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, Thomas L. 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: The 2005 PASCAL Visual Object Classes Challenge. MLCW 2005: 117-176
- 2004
- [c24]Christopher M. Bishop, Ilkay Ulusoy: Object Recognition via Local Patch Labelling. Deterministic and Statistical Methods in Machine Learning 2004: 1-21
- [c23]
- [c22]Balaji Krishnapuram, Christopher M. Bishop, Martin Szummer: Generative models and Bayesian model comparison for shape recognition. IWFHR 2004: 20-25
- [c21]Christopher M. Bishop, Markus Svensén, Goeffrey E. Hinton: Distinguishing text from graphics in on-line handwritten ink. IWFHR 2004: 142-147
- 2003
- [c20]
- 2002
- [c19]Christopher M. Bishop, David J. Spiegelhalter, John M. Winn: VIBES: A Variational Inference Engine for Bayesian Networks. NIPS 2002: 777-784
- [c18]
- 2001
- [j11]Boaz Lerner, W. F. Clocksin, S. Dhanjal, M. A. Hulten, Christopher M. Bishop: Feature representation and signal classification in fluorescence in-situ hybridization image analysis. IEEE Transactions on Systems, Man, and Cybernetics, Part A 31(6): 655-665 (2001)
- [c17]Antony I. T. Rowstron, Neil D. Lawrence, Christopher M. Bishop: Probabilistic Modelling of Replica Divergence. HotOS 2001: 55-60
- [c16]Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor: Optimising Synchronisation Times for Mobile Devices. NIPS 2001: 1401-1408
- 2000
- [c15]
- [c14]
1990 – 1999
- 1999
- [j10]Dan Cornford, Ian T. Nabney, Christopher M. Bishop: Neural Network-Based Wind Vector Retrieval from Satellite Scatterometer Data. Neural Computing and Applications 8(3): 206-217 (1999)
- [j9]Michael E. Tipping, Christopher M. Bishop: Mixtures of Probabilistic Principal Component Analysers. Neural Computation 11(2): 443-482 (1999)
- 1998
- [j8]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: Developments of the generative topographic mapping. Neurocomputing 21(1-3): 203-224 (1998)
- [j7]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: GTM: The Generative Topographic Mapping. Neural Computation 10(1): 215-234 (1998)
- [j6]Christopher M. Bishop, Michael E. Tipping: A Hierarchical Latent Variable Model for Data Visualization. IEEE Trans. Pattern Anal. Mach. Intell. 20(3): 281-293 (1998)
- [c13]
- [c12]Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan: Mixture Representations for Inference and Learning in Boltzmann Machines. UAI 1998: 320-327
- 1997
- [j5]
- [p1]Michael I. Jordan, Christopher M. Bishop: Neural Networks. The Computer Science and Engineering Handbook 1997: 536-556
- [c11]
- [c10]Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan: Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997
- [c9]Paul W. Goldberg, Christopher K. I. Williams, Christopher M. Bishop: Regression with Input-dependent Noise: A Gaussian Process Treatment. NIPS 1997
- 1996
- [j4]
- [c8]Christopher M. Bishop, Cazhaow S. Quazaz: Bayesian Inference of Noise Levels in Regression. ICANN 1996: 59-64
- [c7]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: GTM: A Principled Alternative to the Self-Organizing Map. ICANN 1996: 165-170
- [c6]David Barber, Christopher M. Bishop: Bayesian Model Comparison by Monte Carlo Chaining. NIPS 1996: 333-339
- [c5]Christopher M. Bishop, Cazhaow S. Quazaz: Regression with Input-Dependent Noise: A Bayesian Treatment. NIPS 1996: 347-353
- [c4]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: GTM: A Principled Alternative to the Self-Organizing Map. NIPS 1996: 354-360
- 1995
- [c3]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: EM Optimization of Latent-Variables Density Models. NIPS 1995: 465-471
- 1994
- [j3]Christopher M. Bishop, Paul S. Haynes, Mike E. U. Smith, Tom N. Todd, David L. Trotman: Fast Feedback Control of a High Temperature Fusion Plasma. Neural Computing and Applications 2(3): 148-159 (1994)
- [c2]Christopher M. Bishop, Claire Legleye: Estimating Conditional Probability Densities for Periodic Variables. NIPS 1994: 641-648
- [c1]Christopher M. Bishop: Real-Time Control of a Tokamak Plasma Using Neural Networks. NIPS 1994: 1007-1014
- 1993
- [j2]Christopher M. Bishop, Iain Strachan, John O'Rourke, Geoff Maddison, Paul Thomas: Reconstruction of Tokamak Density Profiles Using Feedforward Networks. Neural Computing and Applications 1(1): 4-16 (1993)
- 1991
- [j1]Christopher M. Bishop: A Fast Procedure for Retraining the Multilayer Perceptron. Int. J. Neural Syst. 2(3): 229-236 (1991)
Coauthor Index
data released under the ODC-BY 1.0 license. See also our legal information page
last updated on 2013-02-25 18:43 CET by the dblp team