Incremental induction of topologically minimal trees
Proceedings of the seventh international conference (1990) on Machine learning
Proceedings of the sixth international workshop on Machine learning
Concept formation knowledge and experience in unsupervised learning
Concept formation knowledge and experience in unsupervised learning
An incremental Bayesian algorithm for categorization
Concept formation knowledge and experience in unsupervised learning
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Conjunctive conceptual clustering: a methodology and experimentation (learning)
Conjunctive conceptual clustering: a methodology and experimentation (learning)
An experimental study of concept formation
An experimental study of concept formation
Unsupervised Learning of Probabilistic Concept Hierarchies
Machine Learning and Its Applications, Advanced Lectures
Structure discovery in medical databases: a conceptual clustering approach
Artificial Intelligence in Medicine
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Editors Choice Article: I2VM: Incremental import vector machines
Image and Vision Computing
Efficiency of self-generating neural networks applied to pattern recognition
Mathematical and Computer Modelling: An International Journal
Machine Learning
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We describe ARACHNE, a concept formation system that, uses explicit constraints on tree structure and local restructuring operators to produce well-formed probabilistic concept trees. We also present a quantitative measure of tree quality and compare the system's performance in artificial and natural domains to that of COBWEB, a well-known concept formation algorithm. The results suggest that ARACHNE frequently constructs higher-quality trees than COBWEB, while still retaining the ability to make accurate predictions.