Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Guiding induction with domain theories
Machine learning
The Utility of Knowledge in Inductive Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Knowledge-based artificial neural networks
Artificial Intelligence
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Machine Learning - Special issue on learning with probabilistic representations
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Applications of Data Mining to Electronic Commerce
Data Mining and Knowledge Discovery
Aggregation of Imprecise and Uncertain Information in Databases
IEEE Transactions on Knowledge and Data Engineering
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Using DAML+OIL to classify intrusive behaviours
The Knowledge Engineering Review
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
International Journal of Hybrid Intelligent Systems
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Two-level Clustering of Web Sites Using Self-Organizing Maps
Neural Processing Letters
Learning classifiers from distributed, ontology-extended data sources
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
AOW '07 Proceedings of the Third Australasian Workshop on Advances in Ontologies - Volume 85
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Many practical applications of machine learning in data-driven scientific discovery commonly call for the exploration of data from multiple points of view that correspond to explicitly specified ontologies. This paper formalizes a class of problems of learning from ontology and data, and explores the design space of learning classifiers from attribute value taxonomies (AVTs) and data. We introduce the notion of AVT-extended data sources and partially specified data. We propose a general framework for learning classifiers from such data sources. Two instantiations of this framework, AVT-based Decision Tree classifier and AVT-based Naïve Bayes classifier are presented. Experimental results show that the resulting algorithms are able to learn robust high accuracy classifiers with substantially more compact representations than those obtained by standard learners.