Multi- Task Learning
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Multi-task learning is an approach to machine learning, that learns a problem together with other related problems at the same time, using a shared representation. This often leads to a better model for the main task, because it allows the learner to use the commonality among the tasks. Therefore, multi-task learning is a kind of inductive transfer. Progol allows arbitrary Prolog programs as background knowledge and arbitrary definite clauses as examples. Despite this...
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Multi-task learning is an approach to machine learning, that learns a problem together with other related problems at the same time, using a shared representation. This often leads to a better model for the main task, because it allows the learner to use the commonality among the tasks. Therefore, multi-task learning is a kind of inductive transfer. Progol allows arbitrary Prolog programs as background knowledge and arbitrary definite clauses as examples. Despite this, in bench-tests the efficiency of Progol compares favourably with FOIL.
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