Deriving Probabilistic SVM Kernels from Exponential Family Approximations to Multivariate Distributions for Count Data

DOI10.1007/978-3-030-23876-6_7zbMath1434.62091OpenAlexW2968303400MaRDI QIDQ5110028

Nizar Bouguila, Nuha Zamzami

Publication date: 15 May 2020

Published in: Unsupervised and Semi-Supervised Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-23876-6_7

zbMATH Keywords

count datamultivariate distributionssupport vector machines (SVMs)activity analysisfinite mixturesFisher scoreprobabilistic kernelsexponential family approximationgeneralized Dirichlet multinomial (GDM)generative/discriminative learningmultinomial compound Dirichlet (DCM)robust hybrid probabilistic learning

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30) Learning and adaptive systems in artificial intelligence (68T05) Statistical aspects of information-theoretic topics (62B10)

Related Items (3)

Online Learning of Inverted Beta-Liouville HMMs for Anomaly Detection in Crowd Scenes ⋮ High-dimensional count data clustering based on an exponential approximation to the multinomial beta-Liouville distribution ⋮ Exact Fisher information of generalized Dirichlet multinomial distribution for count data modeling

Uses Software

• SIFT
• PRMLT

Cites Work

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• Support vector machines with applications
• Finite mixture models and model-based clustering
• Generalized Dirichlet priors for naïve Bayesian classifiers with multinomial models in document classification
• High-dimensional count data clustering based on an exponential approximation to the multinomial beta-Liouville distribution
• Probability for Statistics and Machine Learning
• Divergence measures based on the Shannon entropy
• The EM Algorithm and Extensions, 2E
• Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
• Asymptotic Properties of the Fisher Kernel
• Learning Theory and Kernel Machines
• Computer Vision - ECCV 2004

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