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In machine learning applications, there are scenarios of having no labeled training data, due to the data being rare or too expensive to obtain. In these cases, it is desirable to use readily available labeled data, that is similar to, but not the same as, the domain application of interest. Transfer learning algorithms are used to build high-performance classifiers, when the training data has different...
A transfer learning environment is characterized by a machine learning algorithm being trained with data from one domain (the source domain) and being tested on data from a different domain (the target domain). In a transfer learning scenario, the class probability of the source domain may be different from the class probability of the target domain, which is referred to as "domain class imbalance"...
Previous research focusing on the evaluation of transfer learning algorithms has predominantly used real-world datasets to measure an algorithm's performance. A test with a real-world dataset exposes an algorithm to a single instance of distribution difference between the training (source) and test (target) datasets. These previous works have not measured performance over a wide-range of source and...
Most works covering the topic of transfer learning propose an algorithm to solve a given domain adaptation problem, then test the algorithm using real-world datasets. A test with a real-world dataset represents a single transfer learning test condition, which partially measures an algorithm's performance. Previous research has placed little emphasis on developing a comprehensive and uniform test for...
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