Current activity recognition approaches usually ignore knowledge learned in previous smart environments when training the recognition algorithms for a new smart environment. In this paper, we propose a method of transferring the knowledge of learned activities in multiple physical spaces, e.g. homes A and B, to a new target space, e.g. home C. Transferring the knowledge of learned activities to a target space results in reducing the data collection and annotation period, achieving an accelerated learning pace and exploiting the insights from previous settings. We validate our algorithms using data collected from several smart apartments.