13. A mobile computing device, comprising: means for receiving a finite state machine that includes information that is suitable for conversion into a plurality of boosted decision stumps, wherein each of the plurality of boosted decision stumps evaluate one of a plurality of test conditions; means for converting the information included in the received finite state machine into the plurality of boosted decision stumps that each evaluate one of the plurality of test conditions; means for generating a family of lean classifier models based on the plurality of boosted decision stumps; means for generating an application-type-specific classifier model that includes and prioritizes boosted decision stumps in the plurality of boosted decision stumps that evaluate a subset of test conditions in the plurality of test conditions, wherein the subset of test conditions are determined to evaluate mobile device features that are used by one type of software application, wherein the one type of software application is determined to be suitable for executing on the mobile device; means for selecting a lean classifier model from the family of lean classifier models; and means for applying collected behavior information to the generated application-type-specific classifier model and the selected lean classifier model in parallel to classify a behavior of the mobile computing device.
13. A mobile computing device, comprising: means for receiving a finite state machine that includes information that is suitable for conversion into a plurality of boosted decision stumps, wherein each of the plurality of boosted decision stumps evaluate one of a plurality of test conditions; means for converting the information included in the received finite state machine into the plurality of boosted decision stumps that each evaluate one of the plurality of test conditions; means for generating a family of lean classifier models based on the plurality of boosted decision stumps; means for generating an application-type-specific classifier model that includes and prioritizes boosted decision stumps in the plurality of boosted decision stumps that evaluate a subset of test conditions in the plurality of test conditions, wherein the subset of test conditions are determined to evaluate mobile device features that are used by one type of software application, wherein the one type of software application is determined to be suitable for executing on the mobile device; means for selecting a lean classifier model from the family of lean classifier models; and means for applying collected behavior information to the generated application-type-specific classifier model and the selected lean classifier model in parallel to classify a behavior of the mobile computing device. 15. The mobile computing device of claim 13 , wherein means for generating the application-type-specific classifier model further comprises: means for determining a number of unique test conditions that should be evaluated to classify the behavior without consuming an excessive amount of mobile device resources.