After the features are ready to use, we start to present the learning models for mobile data mining applications. The learning models mainly need to consider two challenges: energy-saving and personalization. In this chapter, we present a hierarchical learning framework for mobile data mining tasks with the goal of energy-saving. Specially, we illustrate the idea with the travel mode detection task. We classify the six modes into wheeled modes and unwheeled modes, where the wheeled modes include outdoor modes (biking) and indoor modes (taking a subway, driving a car, and taking a bus), and the unwheeled modes include walking and jogging. Corresponding to the classification, the hierarchical model consists of three layers. It is based on the results of group feature analysis in the previous chapter. That is, not all sensor data are required for a certain task. For example, we find that only wheeled modes require the full sensor data while the majority of the sensors (except for accelerometer and gyroscope) are turned off in other cases.