Mixture Models and Applications
This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters considers mixture models involving several interesting an...
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2020, 2020
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| Edition: | 1st ed. 2020 |
| Series: | Unsupervised and Semi-Supervised Learning
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- A Gaussian Mixture Model Approach To Classifying Response Types
- Interactive Generation Of Calligraphic Trajectories From Gaussian Mixtures
- Mixture models for the analysis, edition, and synthesis of continuous time series
- Multivariate Bounded Asymmetric Gaussian Mixture Model
- Online Recognition Via A Finite Mixture Of Multivariate Generalized Gaussian Distributions
- L2 Normalized Data Clustering Through the Dirichlet Process Mixture Model of Von Mises Distributions with Localized Feature Selection
- Deriving Probabilistic SVM Kernels From Exponential Family Approximations to Multivariate Distributions for Count Data
- Toward an Efficient Computation of Log-likelihood Functions in Statistical Inference: Overdispersed Count Data Clustering
- A Frequentist Inference Method Based On Finite Bivariate And Multivariate Beta Mixture Models
- Finite Inverted Beta-Liouville Mixture Models with Variational Component Splitting
- Online Variational Learning for Medical Image Data Clustering
- Color Image Segmentation using Semi-Bounded Finite Mixture Models by Incorporating Mean Templates
- Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models
- Flexible Statistical Learning Model For Unsupervised Image Modeling And Segmentation