作者: Tarek Elguebaly , Nizar Bouguila
DOI: 10.1007/978-3-642-21593-3_21
关键词: Overfitting 、 Artificial intelligence 、 Facial recognition system 、 Machine learning 、 Applied mathematics 、 Mixture modeling 、 Mathematics 、 Bayesian probability 、 Gaussian 、 Difficult problem 、 Mixture model 、 Infrared face recognition
摘要: A fully Bayesian approach to analyze infinite multidimensional generalized Gaussian mixture models (IGGM) is developed in this paper. The framework used avoid model overfitting and the assumption adopted difficult problem of finding right number components. utility proposed demonstrated by applying it on texture classification infrared face recognition, while comparing different other approaches.