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- Multi-task_learning abstract "Multi-task learning (MTL) 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. This type of machine learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better.The goal of MTL is to improve the performance of learning algorithms by learning classifiers for multiple tasks jointly. This works particularly well if these tasks have some commonality and are generally slightly under sampled. One example is a spam-filter. Everybody has a slightly different distribution over spam or not-spam emails (e.g. all emails in Russian are spam for me—but not so for my Russian colleagues), yet there is definitely a common aspect across users. Multi-task learning works, because encouraging a classifier (or a modification thereof) to also perform well on a slightly different task is a better regularization than uninformed regularizers (e.g. to enforce that all weights are small).".
- Multi-task_learning wikiPageExternalLink cumulativeLearning.html.
- Multi-task_learning wikiPageExternalLink code.htm.
- Multi-task_learning wikiPageExternalLink multitasklearning.html.
- Multi-task_learning wikiPageExternalLink index.html.
- Multi-task_learning wikiPageID "938663".
- Multi-task_learning wikiPageRevisionID "604518734".
- Multi-task_learning hasPhotoCollection Multi-task_learning.
- Multi-task_learning subject Category:Machine_learning.
- Multi-task_learning comment "Multi-task learning (MTL) 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.".
- Multi-task_learning label "Multi-task learning".
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- Multi-task_learning sameAs Q6934509.
- Multi-task_learning sameAs Q6934509.
- Multi-task_learning wasDerivedFrom Multi-task_learning?oldid=604518734.
- Multi-task_learning isPrimaryTopicOf Multi-task_learning.