A Novel Domain Adaptation Solution to the Transductive Transfer Learning Problem
Report ID: TR-985-15Author: Ash, Jordan
Date: 2015-06-11
Pages: 20
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Abstract:
Most classification algorithms rely on the assumption that training and testing data come from the same distribution. When this assumption is violated, classification performance can be seriously affected. Transfer learning, a new and increasingly popular branch of machine learning, seeks to remedy this potential problem. In this thesis, we introduce a new transductive transfer learning technique that functions by leveraging two separate classification hypotheses to geometrically align the source and target datasets before classification. We show several examples of this technique, and compare it to other methods commonly used in transfer learning.