A general lower bound on the number of examples needed for learning
Information and Computation
Concept Formation During Interactive Theory Revision
Machine Learning - Special issue on evaluating and changing representation
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Information Retrieval
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
First-Order Learning for Web Mining
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Web classification using support vector machine
Proceedings of the 4th international workshop on Web information and data management
Ontology extraction and conceptual modeling for web information
Information modeling for internet applications
Web unit mining: finding and classifying subgraphs of web pages
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
ACM SIGKDD Explorations Newsletter
Biological applications of multi-relational data mining
ACM SIGKDD Explorations Newsletter
Learning Rules for Conceptual Structure on the Web
Journal of Intelligent Information Systems
Cluster-based concept invention for statistical relational learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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We present a new approach to learning hypertext classifiers that combines a statistical text-learning method with a relational rule learner. This approach is well suited to learning in hypertext domains because its statistical component allows it to characterize text in terms of word frequencies, whereas its relational component is able to describe how neighboring documents are related to each other by hyperlinks that connect them. We evaluate our approach by applying it to tasks that involve learning definitions for (i) classes of pages, (ii) particular relations that exist between pairs of pages, and (iii) locating a particular class of information in the internal structure of pages. Our experiments demonstrate that this new approach is able to learn more accurate classifiers than either of its constituent methods alone.