What is he best known for?
The fields of study he is best known for:
- Statistics
- Machine learning
- Artificial intelligence
Geoffrey J. McLachlan mostly deals with Expectation–maximization algorithm, Mixture model, Cluster analysis, Statistics and Artificial intelligence.
His Expectation–maximization algorithm research integrates issues from Combinatorics, Bootstrapping, Applied mathematics and Algorithm, Computation.
The concepts of his Applied mathematics study are interwoven with issues in Monte Carlo method, Mathematical optimization and Finite mixture.
The Finite mixture study which covers Mixture regression that intersects with Mixture modelling, Mixture modeling and Minimum message length.
His Cluster analysis study integrates concerns from other disciplines, such as Dimension and Visualization, Data mining.
Geoffrey J. McLachlan has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
His most cited work include:
- Finite Mixture Models (7555 citations)
- The EM algorithm and extensions (4423 citations)
- Finite mixture models: McLachlan/finite mixture models (3791 citations)
What are the main themes of his work throughout his whole career to date?
Geoffrey J. McLachlan mainly focuses on Mixture model, Statistics, Artificial intelligence, Expectation–maximization algorithm and Cluster analysis.
His Mixture model study combines topics in areas such as Likelihood-ratio test, Multivariate statistics and Applied mathematics.
His Applied mathematics research includes elements of Distribution and Combinatorics.
Geoffrey J. McLachlan has included themes like Machine learning and Pattern recognition in his Artificial intelligence study.
His Expectation–maximization algorithm research integrates issues from Algorithm, Maximum likelihood sequence estimation and Estimation theory.
His Cluster analysis research incorporates themes from Density estimation, Data mining and Computational biology.
He most often published in these fields:
- Mixture model (34.62%)
- Statistics (30.99%)
- Artificial intelligence (24.70%)
What were the highlights of his more recent work (between 2016-2021)?
- Mixture model (34.62%)
- Algorithm (15.98%)
- Artificial intelligence (24.70%)
In recent papers he was focusing on the following fields of study:
Mixture model, Algorithm, Artificial intelligence, Applied mathematics and Normal distribution are his primary areas of study.
His study in Mixture model is interdisciplinary in nature, drawing from both Data mining, Density estimation, Cluster analysis, Expectation–maximization algorithm and False discovery rate.
His research integrates issues of Adversary and MNIST database in his study of Expectation–maximization algorithm.
The various areas that he examines in his Artificial intelligence study include Machine learning, Missing data and Pattern recognition.
Geoffrey J. McLachlan focuses mostly in the field of Pattern recognition, narrowing it down to matters related to Clustering high-dimensional data and, in some cases, Discriminant.
His work on Approximations of π is typically connected to Uniform convergence as part of general Applied mathematics study, connecting several disciplines of science.
Between 2016 and 2021, his most popular works were:
- Deep Gaussian mixture models (32 citations)
- Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution (19 citations)
- EMMIXcskew: An R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions (11 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Normal distribution
His primary areas of study are Mixture model, Algorithm, Applied mathematics, Estimation theory and Skewness.
His Mixture model study combines topics from a wide range of disciplines, such as Probability density function, Data mining, Distribution, Expectation–maximization algorithm and Biological system.
The concepts of his Data mining study are interwoven with issues in Adversary and Rendering.
His Expectation–maximization algorithm research is multidisciplinary, incorporating elements of Norm, MNIST database, Optimization problem and Online algorithm.
His Algorithm research is multidisciplinary, relying on both Model based clustering, Cluster analysis, Multivariate statistics and Feature selection.
Geoffrey J. McLachlan applies his multidisciplinary studies on Applied mathematics and Data modeling in his research.
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