D. M. Titterington

D. Mike Titterington

	2011
j26		Jing-Hao Xue, D. M. Titterington: Median-based image thresholding. Image Vision Comput. 29(9): 631-637 (2011)
j25		Jing-Hao Xue, D. Mike Titterington: t -Tests, F -Tests and Otsu's Methods for Image Thresholding. IEEE Transactions on Image Processing 20(8): 2392-2396 (2011)
	2010
j24		Jing-Hao Xue, D. M. Titterington: On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance. Computational Statistics & Data Analysis 54(2): 438-451 (2010)
j23		Yee Whye Teh, D. Mike Titterington: Preface. Journal of Machine Learning Research - Proceedings Track 9 (2010)
j22		Jing-Hao Xue, D. Mike Titterington: Joint discriminative-generative modelling based on statistical tests for classification. Pattern Recognition Letters 31(9): 1048-1055 (2010)
	2009
j21		Jing-Hao Xue, D. Mike Titterington: Interpretation of hybrid generative/discriminative algorithms. Neurocomputing 72(7-9): 1648-1655 (2009)
j20		Clare A. McGrory, D. M. Titterington, R. Reeves, Anthony N. Pettitt: Variational Bayes for estimating the parameters of a hidden Potts model. Statistics and Computing 19(3): 329-340 (2009)
	2008
j19		Jing-Hao Xue, D. M. Titterington: Comment on "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes". Neural Processing Letters 28(3): 169-187 (2008)
j18		Jing-Hao Xue, D. Mike Titterington: Do unbalanced data have a negative effect on LDA? Pattern Recognition 41(5): 1558-1571 (2008)
j17		Jing-Hao Xue, D. M. Titterington: Short note on two output-dependent hidden Markov models. Pattern Recognition Letters 29(9): 1424-1426 (2008)
	2007
j16		Clare A. McGrory, D. M. Titterington: Variational approximations in Bayesian model selection for finite mixture distributions. Computational Statistics & Data Analysis 51(11): 5352-5367 (2007)
j15		Alexander N. Dolia, Christopher J. Harris, John Shawe-Taylor, D. Mike Titterington: Kernel ellipsoidal trimming. Computational Statistics & Data Analysis 52(1): 309-324 (2007)
	2006
c7		Alexander N. Dolia, Tijl De Bie, Christopher J. Harris, John Shawe-Taylor, D. M. Titterington: The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces. ECML 2006: 630-637
	2005
j14		J. Q. Shi, Roderick Murray-Smith, D. M. Titterington: Hierarchical Gaussian process mixtures for regression. Statistics and Computing 15(1): 31-41 (2005)
c6		D. Mike Titterington: Some Aspects of Latent Structure Analysis. SLSFS 2005: 69-83
	2004
j13		Bo Wang, D. M. Titterington: Lack of Consistency of Mean Field and Variational break Bayes Approximations for State Space Models. Neural Processing Letters 20(3): 151-170 (2004)
c5		Bo Wang, D. M. Titterington: Variational Bayes Estimation of Mixing Coefficients. Deterministic and Statistical Methods in Machine Learning 2004: 281-295
c4		Kazuyuki Tanaka, D. M. Titterington: Probabilistic Image Processing based on the Q-Ising Model by Means of the Mean-Field Method and Loopy Belief Propagation. ICPR (2) 2004: 40-43
c3		Bo Wang, D. M. Titterington: Convergence and Asymptotic Normality of Variational Bayesian Approximations for Expon. UAI 2004: 577-584
	2003
j12		Ernest Fokoué, D. M. Titterington: Mixtures of Factor Analysers. Bayesian Estimation and Inference by Stochastic Simulation. Machine Learning 50(1-2): 73-94 (2003)
c2		Jian Qing Shi, Roderick Murray-Smith, D. M. Titterington, Barak A. Pearlmutter: Filtered Gaussian Processes for Learning with Large Data-Sets. European Summer School on Multi-AgentControl 2003: 128-139
	2000
j11		Keith Humphreys, D. M. Titterington: Improving the Mean-Field Approximation in Belief Networks Using Bahadur's Reparameterisation of the Multivariate Binary Distribution. Neural Processing Letters 12(2): 183-197 (2000)
	1998
j10		A. P. Dunmur, D. M. Titterington: Mean fields and two-dimensional Markov random fields in image analysis. Pattern Anal. Appl. 1(4): 248-260 (1998)
	1997
j9		A. P. Dunmur, D. M. Titterington: Computational Bayesian Analysis of Hidden Markov Mesh Models. IEEE Trans. Pattern Anal. Mach. Intell. 19(11): 1296-1300 (1997)
	1996
c1		A. P. Dunmur, D. M. Titterington: On a Modification to the Mean Field EM Algorithm in Factorial Learning. NIPS 1996: 431-437
	1995
j8		G. Archer, D. M. Titterington: On some Bayesian/regularization methods for image restoration. IEEE Transactions on Image Processing 5(7): 989-995 (1995)
j7		Niall H. Anderson, D. M. Titterington: Beyond the binary Boltzmann machine. IEEE Trans. Neural Netw. Learning Syst. 6(5): 1229-1236 (1995)
	1994
j6		Alison J. Gray, Jim Kay, D. M. Titterington: An Empirical Study of the Simulation of Various Models used for Images. IEEE Trans. Pattern Anal. Mach. Intell. 16(5): 507-513 (1994)
	1993
j5		Wei Qian, D. M. Titterington: Bayesian Image Restoration: An Application to Edge-Preserving Surface Recovery. IEEE Trans. Pattern Anal. Mach. Intell. 15(7): 748-752 (1993)
	1992
j4		Alison J. Gray, Jim Kay, D. M. Titterington: On the estimation of noisy binary Markov random fields. Pattern Recognition 25(7): 749-768 (1992)
	1991
j3		Alan M. Thompson, John C. Brown, Jim Kay, D. M. Titterington: A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization. IEEE Trans. Pattern Anal. Mach. Intell. 13(4): 326-339 (1991)
	1989
j2		D. Mike Titterington: An alternative stochastic supervisor in discriminant analysis. Pattern Recognition 22(1): 91-95 (1989)
	1984
j1		D. M. Titterington: Comments on "Application of the Conditional Population-Mixture Model to Image Segmentation". IEEE Trans. Pattern Anal. Mach. Intell. 6(5): 656-658 (1984)

Coauthor Index

Colors in the list of coauthors

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