Management Science
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Estimating Dependency Structure as a Hidden Variable
Estimating Dependency Structure as a Hidden Variable
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Learning Bayesian networks from incomplete data
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction
Machine Learning - Special issue: Unsupervised learning
Data mining tasks and methods: Probabilistic and casual networks: mining for probabilistic networks
Handbook of data mining and knowledge discovery
Learning Hidden Variable Networks: The Information Bottleneck Approach
The Journal of Machine Learning Research
A recursive method for discriminative mixture learning
Proceedings of the 24th international conference on Machine learning
Generalized additive Bayesian network classifiers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Analysis of user interests based on integrated-graph model
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
On the use of skeletons when learning in Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Robust bayesian linear classifier ensembles
ECML'05 Proceedings of the 16th European conference on Machine Learning
Methods to determine the branching attribute in bayesian multinets classifiers
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
| Hi-index | 0.00 |
We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs). We argue that simple search-and-score algorithms are infeasible for a variety of problems, and introduce a feasible approach in which parameter and structure search is interleaved and expected data is treated as real data. Our approach can be viewed as a combination of (1) the Cheeseman-Stutz asymptotic approximation for model posterior probability and (2) the Expectation-Maximization algorithm. We evaluate our procedure for selecting among MDAGs on synthetic and real examples.