Sentence examples for supervised transfer from inspiring English sources

With this definition, a labeled source and limited labeled target domain is referred to as informed supervised transfer learning.

For example, Daumé [22] and Chattopadhyay [14] define supervised transfer learning as the case of having abundant labeled source data and limited labeled target data, and semi-supervised transfer learning as the case of abundant labeled source data and no labeled target data.

For example, Liu et al. [11] use the notation of supervised transfer learning to denote a fully-labeled source domain and a limited labeled target, while unsupervised transfer learning denotes a mostly labeled source with no target domain labels.

It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis.

In Gong [42] and Blitzer [5], semi-supervised transfer learning is the case of having abundant labeled source data and limited labeled target data, and unsupervised transfer learning is the case of abundant labeled source data and no labeled target data.

AML performed PCR analyses and cloning and supervised data transfer between units.

In both the semi-supervised and transfer learning algorithms, integration of labeled and "auxiliary" data is accomplished by exploiting the relationships between documents and words encoded in the bipartite graph model.

After this, the proposed MKCCA can be implemented in multiple types of learning, such as multi-view learning, supervised learning, semi-supervised learning, and transfer learning, with the reduced data.

Conversely, the work of Cook and Feuz [12] who use supervised and unsupervised transfer learning to denote the presence or absence of labels respectively only in the source domain.

On September 28 , 2010 The Criterion Collection released a special edition of The Thin Red Line on DVD and Blu-ray with a new, restored 4K digital transfer, supervised and approved by Terrence Malick and cinematographer John Toll.

Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes.