A Study of Boosting based Transfer Learning for Activity and Gesture Recognition

Real-world environments are characterized by non-stationary and continuously evolving data. In this thesis, a novel instance transfer technique that adapts a 'Cost-sensitive' variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a 'source' domain to effectively classify unseen instances occurring in a different, but related 'target' domain. The algorithm is evaluated on accelerometer based 3D gesture recognition, smart home activity recognition and text categorization.