May 13, 2017 · In this paper, our goal is to recover the group structure on the sparsity patterns and leverage that information in the sparse learning. Toward ...
Missing: Clusters | Show results with:Clusters
Dec 30, 2017 · In this paper, our goal is to recover the group structure on the sparsity patterns and leverage that information in the sparse learning. Toward ...
3) Clus-MTL: We first learn STL for each task, and then cluster the task parameters using k-means clustering. For each task cluster, we then train a multitask ...
The second class of methods formulates the clustering problem as regularization terms. More specifically, such terms are developed to: (a)penalize small between ...
When multiple tasks share the set of relevant features, learning them jointly in a group drastically improves the quality of relevant feature selection. However ...
Oct 4, 2017 · We propose a novel multi-task learning framework for sparse linear regression, where a full task hierarchy is automatically inferred from the data.
Aug 10, 2019 · Multi-view multi-task learning has recently attracted more and more attention due to its dual-heterogeneity, i.e., each task has ...
Missing: Grouped | Show results with:Grouped
In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, ...
In this work, we formulate a clustering-induced multi-task learning method for feature selection in Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) ...
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Abstract. In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks.