Matrix analysis
Elements of information theory
Elements of information theory
aHUGIN: a system creating adaptive causal probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
A Comparison of New and Old Algorithms for a Mixture EstimationProblem
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
EXPONENTIATED GRADIENT VERSUS GRADIENT DESCENT FOR LINEAR PREDICTORS
EXPONENTIATED GRADIENT VERSUS GRADIENT DESCENT FOR LINEAR PREDICTORS
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Sequential update of Bayesian network structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A unifying review of linear Gaussian models
Neural Computation
An Experimental Comparison of Model-Based Clustering Methods
Machine Learning
Learning from imprecise data: possibilistic graphical models
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Data Mining with Graphical Models
DS '02 Proceedings of the 5th International Conference on Discovery Science
Operations and evaluation measures for learning possibilistic graphical models
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Triple Jump Acceleration for the EM Algorithm
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Multiplicative Updates for Nonnegative Quadratic Programming
Neural Computation
Event detection using "variable module graphs" for home care applications
EURASIP Journal on Applied Signal Processing
Adaptive dynamic probabilistic networks for distributed uncertainty processing
Journal of Experimental & Theoretical Artificial Intelligence
Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks
Wireless Personal Communications: An International Journal
Data Mining and Knowledge Discovery
Learning Bayesian network parameters under incomplete data with domain knowledge
Pattern Recognition
Intelligent Automation in Collaborative Systems
Proceedings of the 2006 conference on Cooperative Systems Design: Seamless Integration of Artifacts and Conversations -- Enhanced Concepts of Infrastructure for Communication
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
An adaptive probabilistic graphical model for representing skills in pbd settings
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Update rules for parameter estimation in continuous time Bayesian network
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Bayesian reasoning for sensor group-queries and diagnosis
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Online learning of Bayesian network parameters with incomplete data
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Accelerating EM: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Exploiting qualitative knowledge in the learning of conditional probabilities of Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
An experimental comparison of several clustering and initialization methods
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Medical informatics: reasoning methods
Artificial Intelligence in Medicine
| Hi-index | 0.00 |
This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [13]. We provide a unified framework for parameter estimation that encompasses both on-line learning, where the model is continuously adapted to new data cases as they arrive, and the more traditional batch learning, where a pre-accumulated set of samples is used in a one-time model selection process. In the batch case, our framework encompasses both the gradient projection algorithm [2, 3] and the EM algorithm [15] for Bayesian networks. The framework also leads to new on-line and batch parameter update schemes, including a parameterized version of EM. We provide both empirical and theoretical results indicating that parameterized EM allows faster convergence to the maximum likelihood parameters than does standard EM.