Applying General Nonconformity Function to Transfer AdaBoost Algorithm
نویسندگان
چکیده
This paper shows that the region classification task can benefit from instance-transfer learning. It proposes to implement a standard region-classification algorithm using a general nonconformity function based on the Transfer AdaBoost algorithm. The experiments show that the new approach produces valid class regions when instances are transferred from a close domain. The conditions for successful instance transfer are empirically derived.
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