Disjunctive models of boolean category learning
Biological Cybernetics
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Connectionist learning procedures
Artificial Intelligence
Processing issues in comparisons of symbolic and connectionist learning systems
Proceedings of the sixth international workshop on Machine learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Machine Learning
Incremental Learning from Noisy Data
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)
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Rule Reduction over Numerical Attributes in Decision Tree Using Multilayer Perceptron
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Processing online analytics with classification and association rule mining
Knowledge-Based Systems
Rule learning by searching on adapted nets
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Combining models from neural networks and inductive learning algorithms
Expert Systems with Applications: An International Journal
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
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AI and connectionist approaches to learning from examples differ in knowledge-base representation and inductive mechanisms. To explore these differences we experiment with a system from each paradigm: ID3 and back-propagation. We compare the systems on the basis of both prediction accuracy and length of training. The systems show distinct performance differences across a variety of domains. We identify aspects of each system that may account for these performance differences. Finally, we suggest paths for cross-paradigm interaction.