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Title of Journal: Int J Mach Learn Cyber

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Abbravation: International Journal of Machine Learning and Cybernetics

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Springer Berlin Heidelberg

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1868-808X

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Bayesian networks for incomplete data analysis in

Authors: Emilie Philippot K C Santosh Abdel Belaïd Yolande Belaïd
Publish Date: 2014/02/14
Volume: 6, Issue: 3, Pages: 347-363
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Abstract

In this paper we study Bayesian network BN for form identification based on partially filled fields It uses electronic inktracing files without having any information about form structure Given a form format the inktracing files are used to build the BN by providing the possible relationships between corresponding fields using conditional probabilities that goes from individual fields up to the complete model construction To simplify the BN we subdivide a single form into three different areas header body and footer and integrate them together where we study three fundamental BN learning algorithms Naive Peter Clark and maximum weighted spanning tree Under this framework we validate it with a realworld industrial problem ie electronic notetaking in form processing The approach provides satisfactory results attesting the interest of BN for exploiting the incomplete form analysis problems in particularMs Emilie Philoppot performed experiments under the supervision of Dr Abdel Belaïd and Dr Yolande Belaïd Dr KC Santosh analysed data and results and wrote a complete paper and responses to the anonymous reviewers in addition to the supplementary results

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