Bayesian Ying-Yang Harmony Learning for Local Factor Analysis: A Comparative Investigation
DOI10.1007/978-3-540-70829-2_10zbMath1176.68166OpenAlexW2098853083MaRDI QIDQ5302481
No author found.
Publication date: 7 January 2009
Published in: Oppositional Concepts in Computational Intelligence (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-540-70829-2_10
model selectionincremental methodsdata smoothingsmall sample sizeautomatic model selectionBayesian ying-Yang harmony learninglocal factor analysistwo-phase implementation
Bayesian problems; characterization of Bayes procedures (62C10) Learning and adaptive systems in artificial intelligence (68T05)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Ordered minimization of total risk in a pattern-recognition problem
- A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving
- Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
- EM algorithms for ML factor analysis
- Estimating the dimension of a model
- RBF nets, mixture experts, and Bayesian Ying-Yang learning
- Akaike's information criterion and recent developments in information complexity
- Mixture Densities, Maximum Likelihood and the EM Algorithm
- Asymptotics for and against cross-validation
- Model Selection and Multimodel Inference
- The minimum description length principle in coding and modeling
- On Estimation of a Probability Density Function and Mode
- A new look at the statistical model identification
This page was built for publication: Bayesian Ying-Yang Harmony Learning for Local Factor Analysis: A Comparative Investigation