Geoffrey J. McLachlan

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35EEMurray A. Jorgensen, Geoffrey J. McLachlan: Wallace's Approach to Unsupervised Learning: The Snob Program. Comput. J. 51(5): 571-578 (2008)
34EEXindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus F. M. Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1): 1-37 (2008)
33EEVladimir Nikulin, Geoffrey J. McLachlan: Merging Algorithm to Reduce Dimensionality in Application to Web-Mining. Australian Conference on Artificial Intelligence 2007: 755-761
32EEShu-Kay Ng, Geoffrey J. McLachlan: Extension of mixture-of-experts networks for binary classification of hierarchical data. Artificial Intelligence in Medicine 41(1): 57-67 (2007)
31EEJangsun Baek, Young Sook Son, Geoffrey J. McLachlan: Segmentation and intensity estimation of microarray images using a gamma-t mixture model. Bioinformatics 23(4): 458-465 (2007)
30EEGeoffrey J. McLachlan, Richard Bean, Liat Ben-Tovim Jones: Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution. Computational Statistics & Data Analysis 51(11): 5327-5338 (2007)
29EEKui Wang, Kelvin K. W. Yau, Andy H. Lee, Geoffrey J. McLachlan: Multilevel survival modelling of recurrent urinary tract infections. Computer Methods and Programs in Biomedicine 87(3): 225-229 (2007)
28EEShu-Kay Ng, Geoffrey J. McLachlan, Andy H. Lee: An incremental EM-based learning approach for on-line prediction of hospital resource utilization. Artificial Intelligence in Medicine 36(3): 257-267 (2006)
27EEGeoffrey J. McLachlan, Richard Bean, Liat Ben-Tovim Jones: A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 22(13): 1608-1615 (2006)
26EEShu-Kay Ng, Geoffrey J. McLachlan, Kui Wang, Liat Ben-Tovim Jones, S.-W. Ng: A Mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics 22(14): 1745-1752 (2006)
25EELiat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu: Mixture Models for Detecting Differentially Expressed Genes in Microarrays. Int. J. Neural Syst. 16(5): 353-362 (2006)
24EEShu-Kay Ng, Geoffrey J. McLachlan: Normalized Gaussian Networks with Mixed Feature Data. Australian Conference on Artificial Intelligence 2005: 879-882
23EERichard Bean, Geoffrey J. McLachlan: Cluster Analysis of High-Dimensional Data: A Case Study. IDEAL 2005: 302-310
22EELiat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu: Application of Mixture Models to Detect Differentially Expressed Genes. IDEAL 2005: 422-431
21EEGeoffrey J. McLachlan, Soong Chang, Jess Mar, Christophe Ambroise, Justin Xi Zhu: On the Simultaneous Use of Clinical and Microarray Expression Data in the Cluster Analysis of Tissue Samples. APBC 2004: 167-171
20EEShu-Kay Ng, Geoffrey J. McLachlan: Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition 37(8): 1573-1589 (2004)
19EEJ. C. Mar, Geoffrey J. McLachlan: Model-Based Clustering in Gene Expression Microarrays: An Application to Breast Cancer Data. APBC 2003: 139-144
18EEShu-Kay Ng, Geoffrey J. McLachlan: Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees. DICTA 2003: 145-154
17EEGeoffrey J. McLachlan, David Peel, Richard Bean: Modelling high-dimensional data by mixtures of factor analyzers. Computational Statistics & Data Analysis 41(3-4): 379-388 (2003)
16EEJ. C. Mar, Geoffrey J. McLachlan: Model-Based Clustering In Gene Expression Microarrays: An Application To Breast Cancer Data. International Journal of Software Engineering and Knowledge Engineering 13(6): 579-592 (2003)
15EEShu-Kay Ng, Geoffrey J. McLachlan: On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures. Statistics and Computing 13(1): 45-55 (2003)
14 Geoffrey J. McLachlan, Richard Bean, David Peel: A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 18(3): 413-422 (2002)
13 Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachlan, Christine E. McLaren: Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. Machine Learning 47(1): 7-34 (2002)
12 Geoffrey J. McLachlan, David Peel: Mixtures of Factor Analyzers. ICML 2000: 599-606
11 Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan: Hierarchical Models for Screening of Iron Deficiency Anemia. ICML 1999: 77-86
10 A. J. Feelders, Soong Chang, Geoffrey J. McLachlan: Mining in the Presence of Selectivity Bias and its Application to Reject Inference. KDD 1998: 199-203
9 Geoffrey J. McLachlan, David Peel: Robust Cluster Analysis via Mixtures of Multivariate t-Distributions. SSPR/SPR 1998: 658-666
8EECharles R. O. Lawoko, Geoffrey J. McLachlan: Bias associated with the discriminant analysis approach to the estimation of mixing proportions. Pattern Recognition 22(6): 763-766 (1989)
7EECharles R. O. Lawoko, Geoffrey J. McLachlan: Further results on discrimination with autocorrelated observations. Pattern Recognition 21(1): 69-72 (1988)
6EECharles R. O. Lawoko, Geoffrey J. McLachlan: Asymptotic error rates of the W and Z statistics when the training observations are dependent. Pattern Recognition 19(6): 467-471 (1986)
5EECharles R. O. Lawoko, Geoffrey J. McLachlan: Discrimination with autocorrelated observations. Pattern Recognition 18(2): 145-149 (1985)
4EECharles R. O. Lawoko, Geoffrey J. McLachlan: Some asymptotic results on the effect of autocorrelation on the error rates of the sample linear discriminant function. Pattern Recognition 16(1): 119-121 (1983)
3EES. Ganesalingam, Geoffrey J. McLachlan: Error rate estimation on the basis of posterior probabilities. Pattern Recognition 12(6): 405-413 (1980)
2EEGeoffrey J. McLachlan: A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification. Pattern Recognition 9(3): 147-149 (1977)
1EEGeoffrey J. McLachlan: Further results on the effect of intraclass correlation among training samples in discriminant analysis. Pattern Recognition 8(4): 273-275 (1976)

Coauthor Index

1Christophe Ambroise [21]
2Jangsun Baek [31]
3Richard Bean [14] [17] [22] [23] [25] [27] [30]
4Igor V. Cadez [11] [13]
5Soong Chang [10] [21]
6A. J. Feelders [10]
7S. Ganesalingam [3]
8Joydeep Ghosh [34]
9David J. Hand [34]
10Liat Ben-Tovim Jones [22] [25] [26] [27] [30]
11Murray A. Jorgensen [35]
12Vipin Kumar [34]
13Charles R. O. Lawoko [4] [5] [6] [7] [8]
14Andy H. Lee [28] [29]
15Bing Liu [34]
16J. C. Mar [16] [19]
17Jess Mar [21]
18Christine E. McLaren [11] [13]
19Hiroshi Motoda [34]
20Angus F. M. Ng [34]
21S.-W. Ng [26]
22Shu-Kay Ng [15] [18] [20] [24] [26] [28] [32]
23Vladimir Nikulin [33]
24David Peel [9] [12] [14] [17]
25J. Ross Quinlan [34]
26Padhraic Smyth [11] [13]
27Young Sook Son [31]
28Michael Steinbach [34]
29Dan Steinberg [34]
30Kui Wang [26] [29]
31Xindong Wu [34]
32Qiang Yang [34]
33Kelvin K. W. Yau [29]
34Philip S. Yu [34]
35Zhi-Hua Zhou [34]
36Justin Xi Zhu [21] [22] [25]

Colors in the list of coauthors

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