Sentence examples for multiple source transfer from inspiring English sources

The work presented in Chattopadhyay [14] is a multiple source transfer learning approach that calculates the source weights as a function of conditional probability differences between the source and target domains attempting to give the most related sources the highest weights.

Since it is difficult to measure the relatedness between any particular source and target domain, Yao [138] proposes to transfer knowledge from multiple source domains using a boosting method in an attempt to minimize the effects of negative transfer from a single unrelated source domain.

The objective is to build a target binary learner for a new class using minimal labeled target data and knowledge transferred from the multiple source learners.

Yao [138] effectively extends the work of Dai [21] (TrAdaBoost) by expanding the transfer boosting algorithm to multiple source domains.

For this case, there is a need to divide the dataset into multiple sources and employ negative transfer methods when using transfer learning algorithm.

In the paper by Perlich [90], a transfer learning approach is employed that uses the weighted outputs of multiple source classifiers to enhance a target classifier trained to predict targeted online display advertising results.

The third transfer category is to transfer knowledge through shared parameters of source and target domain learner models or by creating multiple source learner models and optimally combining the reweighted learners (ensemble learners) to form an improved target learner (examples in Gao [37], Bonilla [8], and Evgeniou [33]).

If multiple source, from which countries does the intelligence originate?

Multiple source localization.

Multiple source databases can be separated by '|'.

Concast routing enables information transfer from multiple sources, through multiple paths, improving user experiences.