For selective instance-based transfer, the proposed TransferBoost algorithm uses a novel form of set-based boosting to determine the individual source instances to transfer in learning the target task. TransferBoost reweights instances from each source task based on their collective transferability to the target task, and then performs regular boosting to adjust individual instance weights.the model transfer networks provides insight into the transfer relationships in these domains. First, the model transfer networks were constructed, one for each domain, using the process described in Section 4.3. The base models for each taskanbsp;...
|Title||:||Selective Knowledge Transfer for Machine Learning|
|Author||:||Eric Robert Eaton|
|Publisher||:||ProQuest - 2009|