Connectionist learning procedures
Artificial Intelligence
Learning nonrecursive definitions of relations with LINUS
EWSL-91 Proceedings of the European working session on learning on Machine learning
The logic of typed feature structures
The logic of typed feature structures
A Polynomial Approach to the Constructive Induction of Structural Knowledge
Machine Learning - Special issue on evaluating and changing representation
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Lazy Induction of Descriptions for Relational Case-Based Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Learning When to Collaborate among Learning Agents
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Similarity Assessment for Relational CBR
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Relational Case-based Reasoning for Carcinogenic Activity Prediction
Artificial Intelligence Review
samap: An user-oriented adaptive system for planning tourist visits
Expert Systems with Applications: An International Journal
Discovering Plausible Explanations of Carcinogenecity in Chemical Compounds
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Usages of Generalization in Case-Based Reasoning
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Experiences Using Clustering and Generalizations for Knowledge Discovery in Melanomas Domain
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Retrieval Based on Self-explicative Memories
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Using explanations for determining carcinogenecity in chemical compounds
Engineering Applications of Artificial Intelligence
Using symbolic descriptions to explain similarity on CBR
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Explanation of a Clustered Case Memory Organization
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Remembering similitude terms in CBR
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Towards Argumentation-based Multiagent Induction
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Argumentation-based Example Interchange for Multiagent Induction
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Classification of melanomas in situ using knowledge discovery with explained case-based reasoning
Artificial Intelligence in Medicine
An ontological approach to represent molecular structure information
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
On learning similarity relations in fuzzy case-based reasoning
Transactions on Rough Sets II
Anti-unification for Unranked Terms and Hedges
Journal of Automated Reasoning
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The aim of relational learning is to develop methods for the induction of hypotheses in representation formalisms that are more expressive than attribute-value representation. Most work on relational learning has been focused on induction in subsets of first order logic like Horn clauses. In this paper we introduce the representation formalism based on feature terms and we introduce the corresponding notions of subsumption and anti-unification. Then we explain INDIE, a heuristic bottom-up learning method that induces class hypotheses, in the form of feature terms, from positive and negative examples. The biases used in INDIE while searching the hypothesis space are explained while describing INDIE's algorithms. The representational bias of INDIE can be summarised in that it makes an intensive use of sorts and sort hierarchy, and in that it does not use negation but focuses on detecting path equalities. We show the results of INDIE in some classical relational datasets showing that it's able to find hypotheses at a level comparable to the original ones. The differences between INDIE's hypotheses and those of the other systems are explained by the bias in searching the hypothesis space and on the representational bias of the hypothesis language of each system.