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Geoffrey J. McLachlan: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. J. C. Mar, Geoffrey J. McLachlan
    Model-Based Clustering in Gene Expression Microarrays: An Application to Breast Cancer Data. [Citation Graph (0, 0)][DBLP]
    APBC, 2003, pp:139-144 [Conf]
  2. 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. [Citation Graph (0, 0)][DBLP]
    APBC, 2004, pp:167-171 [Conf]
  3. Shu-Kay Ng, Geoffrey J. McLachlan
    Normalized Gaussian Networks with Mixed Feature Data. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2005, pp:879-882 [Conf]
  4. Shu-Kay Ng, Geoffrey J. McLachlan
    Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees. [Citation Graph (0, 0)][DBLP]
    DICTA, 2003, pp:145-154 [Conf]
  5. Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan
    Hierarchical Models for Screening of Iron Deficiency Anemia. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:77-86 [Conf]
  6. Geoffrey J. McLachlan, David Peel
    Mixtures of Factor Analyzers. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:599-606 [Conf]
  7. Richard Bean, Geoffrey J. McLachlan
    Cluster Analysis of High-Dimensional Data: A Case Study. [Citation Graph (0, 0)][DBLP]
    IDEAL, 2005, pp:302-310 [Conf]
  8. Liat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu
    Application of Mixture Models to Detect Differentially Expressed Genes. [Citation Graph (0, 0)][DBLP]
    IDEAL, 2005, pp:422-431 [Conf]
  9. A. J. Feelders, Soong Chang, Geoffrey J. McLachlan
    Mining in the Presence of Selectivity Bias and its Application to Reject Inference. [Citation Graph (0, 0)][DBLP]
    KDD, 1998, pp:199-203 [Conf]
  10. Geoffrey J. McLachlan, David Peel
    Robust Cluster Analysis via Mixtures of Multivariate t-Distributions. [Citation Graph (0, 0)][DBLP]
    SSPR/SPR, 1998, pp:658-666 [Conf]
  11. Shu-Kay Ng, Geoffrey J. McLachlan, Andy H. Lee
    An incremental EM-based learning approach for on-line prediction of hospital resource utilization. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2006, v:36, n:3, pp:257-267 [Journal]
  12. Geoffrey J. McLachlan, Richard Bean, Liat Ben-Tovim Jones
    A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2006, v:22, n:13, pp:1608-1615 [Journal]
  13. Geoffrey J. McLachlan, Richard Bean, David Peel
    A mixture model-based approach to the clustering of microarray expression data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2002, v:18, n:3, pp:413-422 [Journal]
  14. 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. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2006, v:22, n:14, pp:1745-1752 [Journal]
  15. Jangsun Baek, Young Sook Son, Geoffrey J. McLachlan
    Segmentation and intensity estimation of microarray images using a gamma-t mixture model. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2007, v:23, n:4, pp:458-465 [Journal]
  16. Liat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu
    Mixture Models for Detecting Differentially Expressed Genes in Microarrays. [Citation Graph (0, 0)][DBLP]
    Int. J. Neural Syst., 2006, v:16, n:5, pp:353-362 [Journal]
  17. J. C. Mar, Geoffrey J. McLachlan
    Model-Based Clustering In Gene Expression Microarrays: An Application To Breast Cancer Data. [Citation Graph (0, 0)][DBLP]
    International Journal of Software Engineering and Knowledge Engineering, 2003, v:13, n:6, pp:579-592 [Journal]
  18. Igor V. Cadez, Padhraic Smyth, Geoffrey J. McLachlan, Christine E. McLaren
    Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:47, n:1, pp:7-34 [Journal]
  19. S. Ganesalingam, Geoffrey J. McLachlan
    Error rate estimation on the basis of posterior probabilities. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 1980, v:12, n:6, pp:405-413 [Journal]
  20. 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. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 1983, v:16, n:1, pp:119-121 [Journal]
  21. Charles R. O. Lawoko, Geoffrey J. McLachlan
    Discrimination with autocorrelated observations. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 1985, v:18, n:2, pp:145-149 [Journal]
  22. Charles R. O. Lawoko, Geoffrey J. McLachlan
    Asymptotic error rates of the W and Z statistics when the training observations are dependent. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 1986, v:19, n:6, pp:467-471 [Journal]
  23. Charles R. O. Lawoko, Geoffrey J. McLachlan
    Further results on discrimination with autocorrelated observations. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 1988, v:21, n:1, pp:69-72 [Journal]
  24. Charles R. O. Lawoko, Geoffrey J. McLachlan
    Bias associated with the discriminant analysis approach to the estimation of mixing proportions. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 1989, v:22, n:6, pp:763-766 [Journal]
  25. Shu-Kay Ng, Geoffrey J. McLachlan
    Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 2004, v:37, n:8, pp:1573-1589 [Journal]
  26. Geoffrey J. McLachlan
    Further results on the effect of intraclass correlation among training samples in discriminant analysis. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 1976, v:8, n:4, pp:273-275 [Journal]
  27. Geoffrey J. McLachlan
    A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 1977, v:9, n:3, pp:147-149 [Journal]
  28. Geoffrey J. McLachlan, David Peel, Richard Bean
    Modelling high-dimensional data by mixtures of factor analyzers. [Citation Graph (0, 0)][DBLP]
    Computational Statistics & Data Analysis, 2003, v:41, n:3-4, pp:379-388 [Journal]
  29. Kui Wang, Kelvin K. W. Yau, Andy H. Lee, Geoffrey J. McLachlan
    Multilevel survival modelling of recurrent urinary tract infections. [Citation Graph (0, 0)][DBLP]
    Computer Methods and Programs in Biomedicine, 2007, v:87, n:3, pp:225-229 [Journal]

  30. Merging Algorithm to Reduce Dimensionality in Application to Web-Mining. [Citation Graph (, )][DBLP]


  31. Ensemble Approach for the Classification of Imbalanced Data. [Citation Graph (, )][DBLP]


  32. Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data. [Citation Graph (, )][DBLP]


  33. Automated High-Dimensional Flow Cytometric Data Analysis. [Citation Graph (, )][DBLP]


  34. Penalized Principal Component Analysis of Microarray Data. [Citation Graph (, )][DBLP]


  35. Extension of mixture-of-experts networks for binary classification of hierarchical data. [Citation Graph (, )][DBLP]


  36. Integrative mixture of experts to combine clinical factors and gene markers. [Citation Graph (, )][DBLP]


  37. Wallace's Approach to Unsupervised Learning: The Snob Program. [Citation Graph (, )][DBLP]


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