Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Robust Classification for Imprecise Environments
Machine Learning
Machine Learning
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Relational Data Mining
Document Processing for Automatic Knowledge Acquisition
IEEE Transactions on Knowledge and Data Engineering
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
Multi-relational Decision Tree Induction
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Automatic Knowledge Acquisition for Spatial Document Interpretation
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
WISDOM++: An Interactive and Adaptive Document Analysis 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
Spatial associative classification: propositional vs structural approach
Journal of Intelligent Information Systems
Learning Recursive Theories in the Normal ILP Setting
Fundamenta Informaticae
Using colour information to understand censorship cards of film archives
International Journal on Document Analysis and Recognition
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
STATE OF APPLICATIONS IN AI RESEARCHES FROM AI*IA 2005
Applied Artificial Intelligence
Learning from Skewed Class Multi-relational Databases
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Learning from Skewed Class Multi-relational Databases
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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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 domain-specific knowledge that can be acquired automatically by applying data mining techniques. The spatial dimension of page layout makes classification methods developed in inductive logic programming (ILP) and multi-relational data mining (MRDM) the most suitable candidates for this specific task. In this paper, both approaches are considered and empirically compared on three different data sets consisting of multi-page articles published in an international journal and historical documents. The ILP method is able to learn recursive logical theories that express dependencies between logical components, while the MRDM method extends the naïve Bayesian classifier to data stored in multiple tables of a relational database. Experimental results confirm the importance of the spatial dimension for this application and show that the ILP method tends to be conservative with a high (low) percentage of omission (commission) errors, while the probabilistic nature of the MRDM method allows us to tradeoff between the two types of error.