Nizar Bouguila mainly focuses on Artificial intelligence, Pattern recognition, Mixture model, Unsupervised learning and Cluster analysis. His Feature selection study in the realm of Artificial intelligence connects with subjects such as Gaussian process. His research integrates issues of Latent Dirichlet allocation, Hierarchical Dirichlet process, Generalized Dirichlet distribution and Gibbs sampling in his study of Pattern recognition.
His Mixture model research is multidisciplinary, incorporating perspectives in Algorithm, Model selection, Automatic summarization and Expectation–maximization algorithm. His study explores the link between Unsupervised learning and topics such as Intrusion detection system that cross with problems in Inference, Anomaly detection and Network security. As a part of the same scientific study, Nizar Bouguila usually deals with the Cluster analysis, concentrating on Stochastic process and frequently concerns with General covariance, Information theory, Covariance and Probability density function.
His primary scientific interests are in Artificial intelligence, Mixture model, Pattern recognition, Cluster analysis and Algorithm. The Artificial intelligence study combines topics in areas such as Data modeling and Machine learning. Nizar Bouguila interconnects Inference, Model selection, Bayesian inference, Statistical model and Applied mathematics in the investigation of issues within Mixture model.
His Pattern recognition research incorporates themes from Hierarchical Dirichlet process and Generalized Dirichlet distribution. As part of the same scientific family, Nizar Bouguila usually focuses on Cluster analysis, concentrating on Data mining and intersecting with Web service. His biological study spans a wide range of topics, including Image, Automatic summarization and Expectation–maximization algorithm.
Mixture model, Artificial intelligence, Pattern recognition, Cluster analysis and Algorithm are his primary areas of study. His Mixture model research is multidisciplinary, relying on both Inference, Model selection, Bayesian inference, Applied mathematics and Feature extraction. His research in Artificial intelligence intersects with topics in Data modeling and Machine learning.
Within one scientific family, he focuses on topics pertaining to Contextual image classification under Pattern recognition, and may sometimes address concerns connected to Generalized Dirichlet distribution. His study in Cluster analysis is interdisciplinary in nature, drawing from both Categorization, Count data, Unsupervised learning and Feature selection. His Algorithm research integrates issues from Feature, Statistical model and Dimensionality reduction.
His main research concerns Mixture model, Artificial intelligence, Cluster analysis, Pattern recognition and Inference. Nizar Bouguila has included themes like Bayesian probability, Applied mathematics, Algorithm, Feature extraction and Robustness in his Mixture model study. As part of his studies on Artificial intelligence, Nizar Bouguila frequently links adjacent subjects like Machine learning.
His Cluster analysis study combines topics from a wide range of disciplines, such as Categorization and Pattern recognition. The various areas that he examines in his Pattern recognition study include Multivariate statistics and Generative model. The concepts of his Inference study are interwoven with issues in Nonparametric statistics, Model selection, Feature selection and Bayes' theorem.
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Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application
N. Bouguila;D. Ziou;J. Vaillancourt.
IEEE Transactions on Image Processing (2004)
High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length
N. Bouguila;D. Ziou.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)
A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering
S. Boutemedjet;N. Bouguila;D. Ziou.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)
Unsupervised selection of a finite Dirichlet mixture model: an MML-based approach
N. Bouguila;D. Ziou.
IEEE Transactions on Knowledge and Data Engineering (2006)
Finite general Gaussian mixture modeling and application to image and video foreground segmentation
Mohand Saïd Allili;Nizar Bouguila;Djemel Ziou.
Journal of Electronic Imaging (2008)
Practical Bayesian estimation of a finite beta mixture through gibbs sampling and its applications
Nizar Bouguila;Djemel Ziou;Ernest Monga.
Statistics and Computing (2006)
Variational Learning for Finite Dirichlet Mixture Models and Applications
Wentao Fan;N. Bouguila;D. Ziou.
IEEE Transactions on Neural Networks (2012)
A hybrid SEM algorithm for high-dimensional unsupervised learning using a finite generalized Dirichlet mixture
N. Bouguila;D. Ziou.
IEEE Transactions on Image Processing (2006)
A study of spam filtering using support vector machines
Ola Amayri;Nizar Bouguila.
Artificial Intelligence Review (2010)
Clustering of Count Data Using Generalized Dirichlet Multinomial Distributions
IEEE Transactions on Knowledge and Data Engineering (2008)
Profile was last updated on December 6th, 2021.
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