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METHODS AND SYSTEMS OF USING BOOSTED DECISION STUMPS AND JOINT FEATURE SELECTION AND CULLING ALGORITHMS FOR THE EFFICIENT CLASSIFICATION OF MOBILE DEVICE BEHAVIORS
METHODS AND SYSTEMS OF USING BOOSTED DECISION STUMPS AND JOINT FEATURE SELECTION AND CULLING ALGORITHMS FOR THE EFFICIENT CLASSIFICATION OF MOBILE DEVICE BEHAVIORS
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机译：有效地对移动设备行为进行分类的使用决策框，联合特征选择和剔除算法的方法和系统
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摘要
Methods and systems for classifying mobile device behavior include configuring a server use a large corpus of mo bile device behaviors to generate a full classifier model that includes a finite state machine suitable for conversion into boosted decis ion stumps and/or which describes all or many of the features relevant to determining whether a mobile device behavior is benign or contributing to the mobile device s degradation over time. A mobile device may receive the full classifier model and use the model to gene rate a full set of boosted decision stumps from which a more focused or lean classifier model is generated by culling the full set to a subset suitable for efficiently determining whether mobile device behavior are benign. Boosted decision stumps may be culled by sele cting all boosted decision stumps that depend upon a limited set of test conditions.
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