International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
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)
Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Complementary discrimination learning: a duality between generalization and discrimination
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
A machine learning and data mining framework to enable evolutionary improvement in trauma triage
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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Fisher (1987a, b) introduced a performance task for conceptual clustering: flexible prediction of arbitrary attribute values, not simply the prediction of a single 'class' attribute. This paper extends earlier analysis by considering the effects of noise and other environmental factors. The degradation in flexible prediction accuracy that results from noise is mitigated by 'preferred' prediction points for individual attributes. Methods that identify these prediction points are inspired by pruning in learning from examples. We extend these noisetolerant techniques to untutored learning. In addition, prediction point preferences shed light on relationships between conceptual clustering, case-based, and default reasoning.