Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Learning to learn
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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Traditional machine learning algorithms operate under the assumption that learning for each new task starts from scratch, thus disregarding knowledge acquired in previous domains. Naturally, if the domains encountered during learning are related, this tabula rasaapproach wastes both data and computational resources in developing hypotheses that could have potentially been recovered by simply slightly modifying previously acquired knowledge. The field of transfer learning(TL), which has witnessed substantial growth in recent years, develops methods that attempt to utilize previously acquired knowledge in a sourcedomain in order to improve the efficiency and accuracy of learning in a new, but related, targetdomain [7,6,1].