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
Bayesian analysis of an inverse Gaussian correlated frailty model
Computational Statistics & Data Analysis
Automatic fuzzy decision network transformation
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Visualization of Possibilistic Potentials
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
An Efficient Algorithm for Naive Possibilistic Classifiers with Uncertain Inputs
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
RSLDI: Restoration of single-sided low-quality document images
Pattern Recognition
Impact of censoring on learning Bayesian networks in survival modelling
Artificial Intelligence in Medicine
A conditional independence algorithm for learning undirected graphical models
Journal of Computer and System Sciences
Visualizing and fuzzy filtering for discovering temporal trajectories of association rules
Journal of Computer and System Sciences
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
Modelling expertise for structure elucidation in organic chemistry using Bayesian networks
Knowledge-Based Systems
Data mining using links in open hypermedia
MIS'02 Proceedings of the 2002 international conference on Metainformatics
On the robustness of Bayesian networks to learning from non-conjugate sampling
International Journal of Approximate Reasoning
Exploring simulated annealing and graphical models for optimization in cognitive wireless networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Assessing the strength of structural changes in cooccurrence graphs
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Learning Bayesian networks from survival data using weighting censored instances
Journal of Biomedical Informatics
A two-stage Bayesian network method for 3D human pose estimation from monocular image sequences
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Fuzzy sets in machine learning and data mining
Applied Soft Computing
Interaction graphs for multivariate binary data
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Possibilistic network-based classifiers: on the reject option and concept drift issues
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Knowledge-Based operations for graphical models in planning
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Probabilistic graphical models for the diagnosis of analog electrical circuits
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Belief revision of product-based causal possibilistic networks
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Structured context prediction: a generic approach
DAIS'10 Proceedings of the 10th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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From the Publisher:The concept of modelling using graph theory has its origin in several scientific areas, notably statistics, physics, genetics, and engineering. The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and is the first to include detailed coverage of possibilistic networks - a relatively new reasoning tool that allows the user to infer results from problems with imprecise data. One major advantage of graphical modelling is that specialised techniques that have been developed in one field can be transferred into others easily. The methods described here are applied in a number of industries, including a recent quality testing programme at a major car manufacturer. Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data Each concept is carefully explained and illustrated by examples Contains all necessary background, including modeling under uncertainty, decomposition of distributions, and graphical representation of decompositions Features applications of learning graphical models from data, and problems for further research Includes a comprehensive bibliography An essential reference for graduate students of graphical modelling, applied statistics, computer science and engineering, as well as researchers and practitioners who use graphical models in their work.