Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
C4.5: programs for machine learning
C4.5: programs for machine learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Robust Classification for Imprecise Environments
Machine Learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Relational Data Mining
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Automatic Knowledge Acquisition for Spatial Document Interpretation
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Structured Document Labeling and Rule Extraction Using a New Recurrent Fuzzy-Neural System
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Construction of generic models of document structures using inference of tree grammars
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Learning Recursive Theories in the Normal ILP Setting
Fundamenta Informaticae
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Document image understanding denotes the recognition of semantically relevant components in the layout extracted from a document image. This recognition process is based on some visual models that can be automatically acquired by applying machine learning techniques. In particular, by properly encapsulating knowledge of the inherent spatial nature of the layout of a document image, spatial relations among logical components of interest can play a key role in the learned models. For this reason, we are investigating the application of (multi-)relational learning techniques, which successfully allows relations between components to be effectively and naturally represented. Goal of this paper is to evaluate and systematically compare two different approaches to relational learning, that is, a statistical approach and a logical approach in the task of document image understanding. For a fair comparison, both methods are tested on the same dataset consisting of multi-page articles published in an international journal. An analysis of pros and cons of both approaches is reported.