Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
Structural methods in pattern analysis
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Automated Concept Acquisition in Noisy Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Based on Conceptual Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Inductive inference in the variable valued predicate logic system vl21: methodology and computer implementation.
Conjunctive conceptual clustering: a methodology and experimentation (learning)
Conjunctive conceptual clustering: a methodology and experimentation (learning)
A conceptual version of the K-means algorithm
Pattern Recognition Letters
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refining Numerical Constants in First Order Logic Theories
Machine Learning - Special issue on multistrategy learning
Multistrategy Theory Revision: Induction and Abductionin INTHELEX
Machine Learning - Special issue on multistrategy learning
The software information base: a server for reuse
The VLDB Journal — The International Journal on Very Large Data Bases
Similarity-Based Queries for Information Retrieval
DNIS '00 Proceedings of the International Workshop on Databases in Networked Information Systems
Resampling vs Reweighting in Boosting a Relational Weak Learner
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
An optimal algorithm for similarity based entity association
ACM-SE 33 Proceedings of the 33rd annual on Southeast regional conference
Document zone content classification and its performance evaluation
Pattern Recognition
An introduction to symbolic data analysis and the SODAS software
Intelligent Data Analysis
Inductive learning from numerical and symbolic data: An integrated framework
Intelligent Data Analysis
Search-intensive concept induction
Evolutionary Computation
On clustering tree structured data with categorical nature
Pattern Recognition
Similarity-Guided Clause Generalization
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
A General Similarity Framework for Horn Clause Logic
Fundamenta Informaticae
Flexible matching for noisy structural descriptions
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Generalization-based similarity for conceptual clustering
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Attribute mapping as a foundation of ontology alignment
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Not far away from home: a relational distance-based approach to understanding images of houses
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
A General Similarity Framework for Horn Clause Logic
Fundamenta Informaticae
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A definition of distance measure between structural descriptions that is based on a probabilistic interpretation of the matching predicate is proposed. It aims at coping with the problem of classification when noise causes both local and structural deformations. The distance measure is defined according to a top-down evaluation scheme: distance between disjunctions of conjuncts, conjunctions, and literals. At the lowest level, the similarity between a feature value in the pattern model (G) and the corresponding value in the observation (Ex) is defined as the probability of observing a greater distortion. The classification problem is approached by means of a multilayered framework in which the cases of single perfect match, no perfect match, and multiple perfect match are treated differently. A plausible solution for the problem of completing the attribute and structure spaces, based on the probabilistic approach, is also given. A comparison with other related works and an application in the domain of layout-based document recognition are presented.