Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Bayesian learning for neural networks
Bayesian learning for neural networks
Efficient approximations for the marginal likelihood of incomplete data given a Bayesian network
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
MML clustering of multi-state, Poisson, vonMises circular and Gaussian distributions
Statistics and Computing
MML Clustering of Continuous-Valued Data Using Gaussian and t Distributions
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
IEEE Transactions on Knowledge and Data Engineering
Globally adaptive region information for automatic color-texture image segmentation
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Remote Sensing
Online clustering via finite mixtures of Dirichlet and minimum message length
Engineering Applications of Artificial Intelligence
On fitting finite dirichlet mixture using ECM and MML
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
MML-Based approach for finite dirichlet mixture estimation and selection
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
On online high-dimensional spherical data clustering and feature selection
Engineering Applications of Artificial Intelligence
Finite asymmetric generalized Gaussian mixture models learning for infrared object detection
Computer Vision and Image Understanding
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We use minimum message length (MML) estimation for mixturemodelling. MML estimates are derived to choose the number ofcomponents in the mixture model to best describe the data andto estimate the parameters of the component densitiesfor Gaussian mixture models. An empirical comparison of criteria prominentin the literature for estimating the number of components in a data setis performed. We have found that MML coding considerationsallows the derivation of usefulresults to guide our implementation of a mixture modelling program. Theseadvantages allow model search to be controlled based on theminimum variance for a component and the amount of data required todistinguish two overlapping components.