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Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. Early versions of MTL were called "hints". In a widely cited 1997 paper, Rich Caruana gave the following characterization:

Attributes	Values
rdf:type	programming language
rdfs:label	Multi-task learning (en)
rdfs:comment	Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. Early versions of MTL were called "hints". In a widely cited 1997 paper, Rich Caruana gave the following characterization: (en)
sameAs	Meta learning
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Subject	Machine learning
gold:hypernym	Approach
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Multi-task_learning?oldid=1068794703&ns=0
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Link from a Wikipage to another Wikipage	Machine learning .NET Framework Adjacency matrix Anti-spam techniques Artificial intelligence Artificial neural network Automated machine learning Statistical classification C Sharp (programming language) General game playing Generalization error Machine learning Decision tree Human-based genetic algorithm Linear combination Robot learning Coercive function Complete metric space Conditional random field Convex optimization Convolutional neural network Orthogonality Overfitting Sparse matrix Kernel methods for vector output Evolutionary computation Feature (machine learning) Feature learning Inductive bias Inner product space Transfer learning Vector-valued function Stochastic gradient descent Loss function Laplacian matrix Multi-label classification Multiclass classification Multitask optimization Regularization (mathematics) Regularization by spectral filtering Bergman kernel dbr:GoogLeNet
has abstract	Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. Early versions of MTL were called "hints". In a widely cited 1997 paper, Rich Caruana gave the following characterization: Multitask 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. In the classification context, MTL aims to improve the performance of multiple classification tasks by learning them jointly. One example is a spam-filter, which can be treated as distinct but related classification tasks across different users. To make this more concrete, consider that different people have different distributions of features which distinguish spam emails from legitimate ones, for example an English speaker may find that all emails in Russian are spam, not so for Russian speakers. Yet there is a definite commonality in this classification task across users, for example one common feature might be text related to money transfer. Solving each user's spam classification problem jointly via MTL can let the solutions inform each other and improve performance. Further examples of settings for MTL include multiclass classification and multi-label classification. Multi-task learning works because regularization induced by requiring an algorithm to perform well on a related task can be superior to regularization that prevents overfitting by penalizing all complexity uniformly. One situation where MTL may be particularly helpful is if the tasks share significant commonalities and are generally slightly under sampled. However, as discussed below, MTL has also been shown to be beneficial for learning unrelated tasks. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Multi-task_learning
is Wikipage redirect of	Applications of multi-task learning Multitask learning
is Link from a Wikipage to another Wikipage of	Applications of multi-task learning Artificial general intelligence Bayesian interpretation of kernel regularization General game playing Cumulative learning Deep learning Kernel methods for vector output Feature hashing Federated learning MTL Matrix regularization Multiclass classification Multitask learning Multitask optimization
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Multi-task_learning

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