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Wai Lam, "Bayesian Network Refinement Via Machine Learning Approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 240251, March, 1998.  
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@article{ 10.1109/34.667882, author = {Wai Lam}, title = {Bayesian Network Refinement Via Machine Learning Approach}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {20}, number = {3}, issn = {01628828}, year = {1998}, pages = {240251}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.667882}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Bayesian Network Refinement Via Machine Learning Approach IS  3 SN  01628828 SP240 EP251 EPD  240251 A1  Wai Lam, PY  1998 KW  Knowledge base refinement KW  uncertainty reasoning KW  Bayesian networks KW  machine learning KW  data mining. VL  20 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—A new approach to refining Bayesian network structures from new data is developed. Most previous work has only considered the refinement of the network's conditional probability parameters and has not addressed the issue of refining the network's structure. We tackle this problem by a machine learning approach based on a formalism known as the Minimum Description Length (MDL) principle. The MDL principle is well suited to this task since it can perform tradeoffs between the accuracy, simplicity, and closeness to the existent structure. Another salient feature of this refinement approach is the capability of refining a network structure using partially specified data. Moreover, a localization scheme is developed for efficient computation of the description lengths since direct evaluation involves exponential time resources.
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