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Geoffrey J. McLachlan
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2020 – today
- 2024
- [j73]Faïcel Chamroukhi
, Nhat Thien Pham, Van Hà Hoang, Geoffrey J. McLachlan:
Functional mixtures-of-experts. Stat. Comput. 34(3): 98 (2024) - 2023
- [j72]You-Gan Wang
, Jinran Wu
, Zhi-Hua Hu
, Geoffrey J. McLachlan:
A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognit. 133: 108989 (2023) - 2022
- [j71]Daniel Ahfock
, Saumyadipta Pyne, Geoffrey J. McLachlan:
Statistical file-matching of non-Gaussian data: A game theoretic approach. Comput. Stat. Data Anal. 168: 107387 (2022) - [j70]Sharon X. Lee, Geoffrey J. McLachlan
:
An overview of skew distributions in model-based clustering. J. Multivar. Anal. 188: 104853 (2022) - [i10]Faïcel Chamroukhi, Nhat Thien Pham, Van Hà Hoang, Geoffrey J. McLachlan:
Functional Mixtures-of-Experts. CoRR abs/2202.02249 (2022) - [i9]Ziyang Lyu
, Daniel Ahfock, Geoffrey J. McLachlan:
Some Simulation and Empirical Results for Semi-Supervised Learning of the Bayes Rule of Allocation. CoRR abs/2210.13785 (2022) - 2021
- [j69]Sharon X. Lee, Tsung-I Lin, Geoffrey J. McLachlan
:
Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions. Adv. Data Anal. Classif. 15(2): 481-512 (2021) - [j68]Daniel Ahfock
, Geoffrey J. McLachlan:
Harmless label noise and informative soft-labels in supervised classification. Comput. Stat. Data Anal. 161: 107253 (2021) - [j67]Mohadeseh Alsadat Farzammehr, Mohammad Reza Zadkarami
, Geoffrey J. McLachlan:
Skew-normal generalized spatial panel data model. Commun. Stat. Simul. Comput. 50(11): 3286-3314 (2021) - [j66]Daniel Ahfock
, Saumyadipta Pyne, Geoffrey J. McLachlan:
Data fusion using factor analysis and low-rank matrix completion. Stat. Comput. 31(5): 58 (2021) - [j65]Sharon X. Lee, Geoffrey J. McLachlan
, Kaleb L. Leemaqz:
Multi-node Expectation-Maximization algorithm for finite mixture models. Stat. Anal. Data Min. 14(4): 297-304 (2021) - [i8]Daniel Ahfock, Geoffrey J. McLachlan:
Harmless label noise and informative soft-labels in supervised classification. CoRR abs/2104.02872 (2021) - [i7]Daniel Ahfock, Geoffrey J. McLachlan:
Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review. CoRR abs/2104.04046 (2021) - 2020
- [j64]Hien Duy Nguyen
, Florence Forbes, Geoffrey J. McLachlan:
Mini-batch learning of exponential family finite mixture models. Stat. Comput. 30(4): 731-748 (2020) - [j63]Daniel Ahfock
, Geoffrey J. McLachlan
:
An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified. Stat. Comput. 30(6): 1779-1790 (2020) - [i6]Geoffrey J. McLachlan, Daniel Ahfock:
Estimation of Classification Rules from Partially Classified Data. CoRR abs/2004.06237 (2020) - [i5]TrungTin Nguyen
, Hien Duy Nguyen, Faicel Chamroukhi, Geoffrey J. McLachlan:
An l1-oracle inequality for the Lasso in mixture-of-experts regression models. CoRR abs/2009.10622 (2020)
2010 – 2019
- 2019
- [j62]Sharon X. Lee
, Kaleb L. Leemaqz, Geoffrey J. McLachlan:
PPEM: Privacy-preserving EM learning for mixture models. Concurr. Comput. Pract. Exp. 31(24) (2019) - [j61]Shu-Kay Ng
, Richard Tawiah
, Geoffrey J. McLachlan
:
Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework. Pattern Recognit. 88: 261-271 (2019) - [j60]Cinzia Viroli
, Geoffrey J. McLachlan
:
Deep Gaussian mixture models. Stat. Comput. 29(1): 43-51 (2019) - 2018
- [j59]Luke R. Lloyd-Jones
, Hien Duy Nguyen
, Geoffrey J. McLachlan
:
A globally convergent algorithm for lasso-penalized mixture of linear regression models. Comput. Stat. Data Anal. 119: 19-38 (2018) - [j58]Hien Duy Nguyen
, Dianhui Wang, Geoffrey J. McLachlan
:
Randomized mixture models for probability density approximation and estimation. Inf. Sci. 467: 135-148 (2018) - [j57]Andrew T. Jones, Hien Duy Nguyen
, Geoffrey J. McLachlan:
logKDE: log-transformed kernel density estimation. J. Open Source Softw. 3(28): 870 (2018) - [j56]Hien Duy Nguyen
, Jeremy F. P. Ullmann, Geoffrey J. McLachlan
, Venkatakaushik Voleti, Wenze Li
, Elizabeth M. C. Hillman
, David C. Reutens
, Andrew L. Janke
:
Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling. Stat. Anal. Data Min. 11(1): 5-16 (2018) - [j55]Sharon X. Lee
, Kaleb L. Leemaqz, Geoffrey J. McLachlan
:
A Block EM Algorithm for Multivariate Skew Normal and Skew $t$ -Mixture Models. IEEE Trans. Neural Networks Learn. Syst. 29(11): 5581-5591 (2018) - [c32]Hien D. Nguyen
, Andrew T. Jones
, Geoffrey J. McLachlan:
Positive Data Kernel Density Estimation via the LogKDE Package for R. AusDM 2018: 269-280 - 2017
- [j54]Hien Duy Nguyen
, Geoffrey J. McLachlan
, Pierre Orban, Pierre Bellec, Andrew L. Janke
:
Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data. Neural Comput. 29(4): 990-1020 (2017) - [c31]Kaleb L. Leemaqz, Sharon X. Lee, Geoffrey J. McLachlan
:
Private Distributed Three-Party Learning of Gaussian Mixture Models. ATIS 2017: 75-87 - [c30]Kaleb L. Leemaqz, Sharon X. Lee, Geoffrey J. McLachlan
:
Corruption-Resistant Privacy Preserving Distributed EM Algorithm for Model-Based Clustering. TrustCom/BigDataSE/ICESS 2017: 1082-1089 - [i4]Hien D. Nguyen, Geoffrey J. McLachlan:
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization-Minimization Algorithm Approach. CoRR abs/1705.04651 (2017) - [i3]Cinzia Viroli, Geoffrey J. McLachlan:
Deep Gaussian Mixture Models. CoRR abs/1711.06929 (2017) - 2016
- [j53]Hien Duy Nguyen
, Geoffrey J. McLachlan
, Ian A. Wood
:
Mixtures of spatial spline regressions for clustering and classification. Comput. Stat. Data Anal. 93: 76-85 (2016) - [j52]Hien Duy Nguyen
, Geoffrey J. McLachlan
:
Laplace mixture of linear experts. Comput. Stat. Data Anal. 93: 177-191 (2016) - [j51]Hien Duy Nguyen
, Geoffrey J. McLachlan
:
Maximum likelihood estimation of triangular and polygonal distributions. Comput. Stat. Data Anal. 102: 23-36 (2016) - [j50]Hien Duy Nguyen
, Geoffrey J. McLachlan
:
Linear mixed models with marginally symmetric nonparametric random effects. Comput. Stat. Data Anal. 103: 151-169 (2016) - [j49]Daniel Ahfock
, Saumyadipta Pyne, Sharon X. Lee, Geoffrey J. McLachlan
:
Partial identification in the statistical matching problem. Comput. Stat. Data Anal. 104: 79-90 (2016) - [j48]Tsung-I Lin, Geoffrey J. McLachlan
, Sharon X. Lee:
Extending mixtures of factor models using the restricted multivariate skew-normal distribution. J. Multivar. Anal. 143: 398-413 (2016) - [j47]Hien Duy Nguyen
, Luke R. Lloyd-Jones
, Geoffrey J. McLachlan
:
A Universal Approximation Theorem for Mixture-of-Experts Models. Neural Comput. 28(12): 2585-2593 (2016) - [j46]Sharon X. Lee, Geoffrey J. McLachlan
:
Finite mixtures of canonical fundamental skew t-distributions - The unification of the restricted and unrestricted skew t-mixture models. Stat. Comput. 26(3): 573-589 (2016) - [j45]Hien Duy Nguyen
, Luke R. Lloyd-Jones
, Geoffrey J. McLachlan
:
A Block Minorization-Maximization Algorithm for Heteroscedastic Regression. IEEE Signal Process. Lett. 23(8): 1131-1135 (2016) - [c29]Sharon X. Lee, Geoffrey J. McLachlan
:
Unsupervised Component-Wise EM Learning for Finite Mixtures of Skew t-distributions. ADMA 2016: 692-699 - [c28]Shu-Kay Ng
, Geoffrey J. McLachlan
:
Finding group structures in "Big Data" in healthcare research using mixture models. BIBM 2016: 1214-1219 - [c27]Sharon X. Lee, Kaleb L. Leemaqz, Geoffrey J. McLachlan
:
A Simple Parallel EM Algorithm for Statistical Learning via Mixture Models. DICTA 2016: 1-8 - [i2]Sharon X. Lee, Kaleb L. Leemaqz, Geoffrey J. McLachlan:
A block EM algorithm for multivariate skew normal and skew t-mixture models. CoRR abs/1608.02797 (2016) - 2015
- [j44]Hien Duy Nguyen
, Geoffrey J. McLachlan
:
Maximum likelihood estimation of Gaussian mixture models without matrix operations. Adv. Data Anal. Classif. 9(4): 371-394 (2015) - 2014
- [j43]Dankmar Böhning, Christian Hennig, Geoffrey J. McLachlan, Paul D. McNicholas
:
The 2nd special issue on advances in mixture models. Comput. Stat. Data Anal. 71: 1-2 (2014) - [j42]Shu-Kay Ng
, Geoffrey J. McLachlan:
Mixture models for clustering multilevel growth trajectories. Comput. Stat. Data Anal. 71: 43-51 (2014) - [j41]Sharon X. Lee, Geoffrey J. McLachlan
:
Finite mixtures of multivariate skew t-distributions: some recent and new results. Stat. Comput. 24(2): 181-202 (2014) - [j40]Hien Duy Nguyen
, Geoffrey J. McLachlan
, Nicolas Cherbuin
, Andrew L. Janke
:
False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields. IEEE Trans. Medical Imaging 33(8): 1735-1748 (2014) - [j39]Geoffrey J. McLachlan
, Suren I. Rathnayake:
On the number of components in a Gaussian mixture model. WIREs Data Mining Knowl. Discov. 4(5): 341-355 (2014) - [c26]Hien Duy Nguyen
, Geoffrey J. McLachlan
:
Asymptotic inference for hidden process regression models. SSP 2014: 256-259 - 2013
- [j38]Sharon X. Lee, Geoffrey J. McLachlan
:
On mixtures of skew normal and skew t-distributions. Adv. Data Anal. Classif. 7(3): 241-266 (2013) - [j37]Kaye E. Basford
, Geoffrey J. McLachlan
, Suren I. Rathnayake:
On the classification of microarray gene-expression data. Briefings Bioinform. 14(4): 402-410 (2013) - [j36]Sharon X. Lee, Geoffrey J. McLachlan
:
Model-based clustering and classification with non-normal mixture distributions. Stat. Methods Appl. 22(4): 427-454 (2013) - [j35]Sharon X. Lee, Geoffrey J. McLachlan
:
Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions". Stat. Methods Appl. 22(4): 473-479 (2013) - [c25]Mingzhu Sun, Geoffrey J. McLachlan
:
A common factor-analytic model for classification. BIBM 2013: 19-24 - [c24]Shu-Kay Ng
, Geoffrey J. McLachlan
:
Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes. BIBM 2013: 267-272 - [c23]Hien Duy Nguyen
, Andrew L. Janke
, Nicolas Cherbuin
, Geoffrey J. McLachlan
, Perminder S. Sachdev
, Kaarin Anstey
:
Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies. DICTA 2013: 1-8 - [e1]Guo-Zheng Li, Sunghoon Kim, Michael Hughes, Geoffrey J. McLachlan, Hongye Sun, Xiaohua Hu, Habtom W. Ressom, Baoyan Liu, Michael N. Liebman:
2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, December 18-21, 2013. IEEE Computer Society 2013, ISBN 978-1-4799-1309-1 [contents] - 2012
- [j34]Kui Wang, Shu-Kay Ng
, Geoffrey J. McLachlan
:
Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects. BMC Bioinform. 13: 300 (2012) - [j33]Gabor Melli, Xindong Wu, Paul Beinat, Francesco Bonchi, Longbing Cao
, Rong Duan, Christos Faloutsos, Rayid Ghani, Brendan Kitts, Bart Goethals
, Geoffrey J. McLachlan
, Jian Pei
, Ashok Srivastava, Osmar R. Zaïane:
Top-10 Data Mining Case Studies. Int. J. Inf. Technol. Decis. Mak. 11(2): 389-400 (2012) - [p2]Geoffrey J. McLachlan
:
An Enduring Interest in Classification: Supervised and Unsupervised. Journeys to Data Mining 2012: 147-171 - 2011
- [j32]Jangsun Baek, Geoffrey J. McLachlan
:
Mixtures of common t-factor analyzers for clustering high-dimensional microarray data. Bioinform. 27(9): 1269-1276 (2011) - [j31]Vladimir Nikulin, Tian-Hsiang Huang, Geoffrey J. McLachlan
:
Classification of High-Dimensional microarray Data with a Two-Step Procedure via a Wilcoxon Criterion and Multilayer Perceptron. Int. J. Comput. Intell. Appl. 10(1): 1-14 (2011) - 2010
- [j30]Kim-Anh Lê Cao
, Emmanuelle Meugnier
, Geoffrey J. McLachlan
:
Integrative mixture of experts to combine clinical factors and gene markers. Bioinform. 26(9): 1192-1198 (2010) - [j29]Jangsun Baek, Geoffrey J. McLachlan
, Lloyd K. Flack:
Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data. IEEE Trans. Pattern Anal. Mach. Intell. 32(7): 1298-1309 (2010) - [c22]Vladimir Nikulin, Tian-Hsiang Huang, Geoffrey J. McLachlan
:
A comparative study of two matrix factorization methods applied to the classification of gene expression data. BIBM 2010: 618-621 - [c21]Vladimir Nikulin, Geoffrey J. McLachlan:
On the Gradient-based Algorithm for Matrix Factorization Applied to Dimensionality Reduction. BIOINFORMATICS 2010: 147-152 - [c20]Geoffrey J. McLachlan:
Assessing the Significance of Groups in High-Dimensional Data. ICDM 2010: 6 - [c19]Vladimir Nikulin, Geoffrey J. McLachlan
:
Identifying fiber bundles with regularised к-means clustering applied to the grid-based data. IJCNN 2010: 1-8 - [c18]Saumyadipta Pyne, Xinli Hu, Kui Wang, Elizabeth Rossin, Tsung-I Lin, Lisa Maier, Clare Baecher-Allan, Geoffrey J. McLachlan, Pablo Tamayo, David Hafler, Philip L. De Jager, Jill P. Mesirov:
Automated High-Dimensional Flow Cytometric Data Analysis. RECOMB 2010: 577 - [i1]Vladimir Nikulin, Tian-Hsiang Huang, Shu-Kay Ng, Suren I. Rathnayake, Geoffrey J. McLachlan:
A Very Fast Algorithm for Matrix Factorization. CoRR abs/1011.0506 (2010)
2000 – 2009
- 2009
- [c17]Vladimir Nikulin, Geoffrey J. McLachlan
, Shu-Kay Ng
:
Ensemble Approach for the Classification of Imbalanced Data. Australasian Conference on Artificial Intelligence 2009: 291-300 - [c16]Vladimir Nikulin, Geoffrey J. McLachlan
:
Penalized Principal Component Analysis of Microarray Data. CIBB 2009: 82-96 - [c15]Kui Wang, Shu-Kay Ng
, Geoffrey J. McLachlan
:
Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data. DICTA 2009: 526-531 - [c14]Vladimir Nikulin, Geoffrey J. McLachlan:
Classification of Imbalanced Marketing Data with Balanced Random Sets. KDD Cup 2009: 89-100 - 2008
- [j28]Murray A. Jorgensen, Geoffrey J. McLachlan
:
Wallace's Approach to Unsupervised Learning: The Snob Program. Comput. J. 51(5): 571-578 (2008) - [j27]Xindong 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 S. Steinbach
, David J. Hand
, Dan Steinberg:
Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1): 1-37 (2008) - [c13]Geoffrey J. McLachlan
, Jangsun Baek:
Clustering of High-Dimensional Data via Finite Mixture Models. GfKl 2008: 33-44 - 2007
- [j26]Shu-Kay Ng
, Geoffrey J. McLachlan
:
Extension of mixture-of-experts networks for binary classification of hierarchical data. Artif. Intell. Medicine 41(1): 57-67 (2007) - [j25]Jangsun Baek, Young Sook Son, Geoffrey J. McLachlan
:
Segmentation and intensity estimation of microarray images using a gamma-t mixture model. Bioinform. 23(4): 458-465 (2007) - [j24]Kui Wang
, Kelvin K. W. Yau, Andy H. Lee, Geoffrey J. McLachlan
:
Multilevel survival modelling of recurrent urinary tract infections. Comput. Methods Programs Biomed. 87(3): 225-229 (2007) - [j23]Geoffrey J. McLachlan, Richard W. Bean
, Liat Ben-Tovim Jones:
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution. Comput. Stat. Data Anal. 51(11): 5327-5338 (2007) - [j22]Kui Wang
, Kelvin K. W. Yau, Andy H. Lee, Geoffrey J. McLachlan
:
Two-component Poisson mixture regression modelling of count data with bivariate random effects. Math. Comput. Model. 46(11-12): 1468-1476 (2007) - [c12]Vladimir Nikulin, Geoffrey J. McLachlan
:
Merging Algorithm to Reduce Dimensionality in Application to Web-Mining. Australian Conference on Artificial Intelligence 2007: 755-761 - 2006
- [j21]Shu-Kay Ng
, Geoffrey J. McLachlan
, Andy H. Lee:
An incremental EM-based learning approach for on-line prediction of hospital resource utilization. Artif. Intell. Medicine 36(3): 257-267 (2006) - [j20]Geoffrey J. McLachlan
, Richard W. Bean
, Liat Ben-Tovim Jones:
A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinform. 22(13): 1608-1615 (2006) - [j19]Shu-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. Bioinform. 22(14): 1745-1752 (2006) - [j18]Liat 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) - 2005
- [c11]Shu-Kay Ng
, Geoffrey J. McLachlan
:
Normalized Gaussian Networks with Mixed Feature Data. Australian Conference on Artificial Intelligence 2005: 879-882 - [c10]Richard Bean, Geoffrey J. McLachlan:
Cluster Analysis of High-Dimensional Data: A Case Study. IDEAL 2005: 302-310 - [c9]Liat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu:
Application of Mixture Models to Detect Differentially Expressed Genes. IDEAL 2005: 422-431 - 2004
- [j17]Shu-Kay Ng
, Geoffrey J. McLachlan
:
Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognit. 37(8): 1573-1589 (2004) - [j16]Shu-Kay Ng
, Geoffrey J. McLachlan
:
Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification. IEEE Trans. Neural Networks 15(3): 738-749 (2004) - [c8]Geoffrey 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 - 2003
- [j15]Geoffrey J. McLachlan
, David Peel, Richard W. Bean
:
Modelling high-dimensional data by mixtures of factor analyzers. Comput. Stat. Data Anal. 41(3-4): 379-388 (2003) - [j14]J. C. Mar, Geoffrey J. McLachlan
:
Model-Based Clustering In Gene Expression Microarrays: An Application To Breast Cancer Data. Int. J. Softw. Eng. Knowl. Eng. 13(6): 579-592 (2003) - [j13]Shu-Kay Ng
, Geoffrey J. McLachlan
:
On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures. Stat. Comput. 13(1): 45-55 (2003) - [c7]J. C. Mar, Geoffrey J. McLachlan:
Model-Based Clustering in Gene Expression Microarrays: An Application to Breast Cancer Data. APBC 2003: 139-144 - [c6]Shu-Kay Ng, Geoffrey J. McLachlan:
Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees. DICTA 2003: 145-154 - 2002
- [j12]Geoffrey J. McLachlan
, Richard W. Bean
, David Peel:
A mixture model-based approach to the clustering of microarray expression data. Bioinform. 18(3): 413-422 (2002) - [j11]Igor V. Cadez, Padhraic Smyth
, Geoffrey J. McLachlan
, Christine E. McLaren:
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. Mach. Learn. 47(1): 7-34 (2002) - 2000
- [b1]Geoffrey J. McLachlan
, David Peel:
Finite Mixture Models. Wiley Series in Probability and Statistics, Wiley 2000, ISBN 978-0-47100626-8, pp. 1-427 - [j10]David Peel, Geoffrey J. McLachlan
:
Robust mixture modelling using the t distribution. Stat. Comput. 10(4): 339-348 (2000) - [c5]Geoffrey J. McLachlan, David Peel:
Mixtures of Factor Analyzers. ICML 2000: 599-606
1990 – 1999
- 1999
- [c4]Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan:
Hierarchical Models for Screening of Iron Deficiency Anemia. ICML 1999: 77-86 - 1998
- [c3]Geoffrey J. McLachlan, David Peel:
MIXFIT: an algorithm for the automatic fitting and testing of normal mixture models. ICPR 1998: 553-557 - [c2]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 - [c1]Geoffrey J. McLachlan, David Peel:
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions. SSPR/SPR 1998: 658-666 - 1996
- [j9]Geoffrey J. McLachlan
, David Peel, W. J. Whiten:
Maximum likelihood clustering via normal mixture models. Signal Process. Image Commun. 8(2): 105-111 (1996)
1980 – 1989
- 1989
- [j8]Charles R. O. Lawoko, Geoffrey J. McLachlan
:
Bias associated with the discriminant analysis approach to the estimation of mixing proportions. Pattern Recognit. 22(6): 763-766 (1989) - 1988
- [j7]Charles R. O. Lawoko, Geoffrey J. McLachlan
:
Further results on discrimination with autocorrelated observations. Pattern Recognit. 21(1): 69-72 (1988) - 1986
- [j6]Charles R. O. Lawoko, Geoffrey J. McLachlan
:
Asymptotic error rates of the W and Z statistics when the training observations are dependent. Pattern Recognit. 19(6): 467-471 (1986) - 1985
- [j5]Charles R. O. Lawoko, Geoffrey J. McLachlan:
Discrimination with autocorrelated observations. Pattern Recognit. 18(2): 145-149 (1985) - 1983
- [j4]Charles 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 Recognit. 16(1): 119-121 (1983) - 1982
- [p1]Geoffrey J. McLachlan:
9 The classification and mixture maximum likelihood approaches to cluster analysis. Classification, Pattern Recognition and Reduction of Dimensionality 1982: 199-208 - 1980
- [j3]S. Ganesalingam, Geoffrey J. McLachlan:
Error rate estimation on the basis of posterior probabilities. Pattern Recognit. 12(6): 405-413 (1980)
1970 – 1979
- 1977
- [j2]Geoffrey J. McLachlan
:
A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification. Pattern Recognit. 9(3): 147-149 (1977) - 1976
- [j1]Geoffrey J. McLachlan
:
Further results on the effect of intraclass correlation among training samples in discriminant analysis. Pattern Recognit. 8(4): 273-275 (1976)
Coauthor Index
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