Semisupervised Multitask Learning

By Qiuhua Liu, Xuejun Liao, J. Stack, Lawrence Carin. 2009

93 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

An Overview of Multi-task Learning (2018, neutral): “Formulti-tasksemi-supervisedclassification,amethodpro-posedin[63,64]followsthetask-clusteringap-proachtodotaskclusteringondifferenttasksbased onarelaxedDirichletprocess,whileineachtask, randomwalkisusedtoexploitusefulinformation containedintheunlabeleddata.”
A Survey on Multi-Task Learning (2017, methodology): “[123] combine semi-supervised learning and active learning to utilize unlabeled data for MTL by using the Fisher information as a criterion to select unlabeled data to acquire their labels with the semi-supervised multi-task classification model [117], [118] as the classifier for multiple tasks.”
Multi-Task Averaging (2011, background): “k of Bakker and Heskes (2003) some of the model parameters are shared amongst tasks, while others are connected through a joint prior. Xue et al. (2007) drew the t from a Dirichlet process prior, and Liu et al. (2009) placed a joint distribution over the tasks’ parameters in a semi-supervised framew”
A Graphbased Framework for Multi-Task Multi-View Learning (2011, methodology): “Therefore, we compare with the following algorithms: In our experiments, we apply SVM-2K on the multiple views of each task respectively; we apply SMTL and CASO on the common views of all the tasks.”
Cross-Domain Multitask Learning with Latent Probit Models (2012, methodology): “, 2007) is based on airborne synthetic-aperture radar (SAR) data and the underwater mine detection problem(4) (Liu et al., 2009) is based on synthetic-aperture sonar (SAS) data.”
Convex Discriminative Multitask Clustering (2013, background): “The second class shares common model parameters, such as placing a common prior across tasks within the hierarchical Bayesian framework [13], [14], [15], [16], learning the differences of the task-specific models in Frobenius norms under the regularization framework [17], [18], [19], etc.”
Learning Multiple Tasks using Manifold Regularization (2010, background): “This notion of relatedness is usually incorporated in the form of a regularizer [4] [16] [13] or a prior [15] [22] [21].”