Machine learning of inductive bias
Machine learning of inductive bias
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Computation
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Learning to learn
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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Marketing Science
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The Journal of Machine Learning Research
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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The Journal of Machine Learning Research
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IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Selective transfer of task knowledge using stochastic noise
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Consolidation using context-sensitive multiple task learning
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Machine lifelong learning: challenges and benefits for artificial general intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
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Neurocomputing
Image morphing: transfer learning between tasks that have multiple outputs
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer which uses a single output neural network and additional contextual inputs for learning multiple tasks. Motivated by problems with the application of MTL networks to machine lifelong learning systems, csMTL encoding of multiple task examples was developed and found to improve predictive performance. As evidence, the csMTL method is tested on seven task domains and shown to produce hypotheses for primary tasks that are often better than standard MTL hypotheses when learning in the presence of related and unrelated tasks. We argue that the reason for this performance improvement is a reduction in the number of effective free parameters in the csMTL network brought about by the shared output node and weight update constraints due to the context inputs. An examination of IDT and SVM models developed from csMTL encoded data provides initial evidence that this improvement is not shared across all machine learning models.