Abstract: Resource allocation for machine learning is described such as for selecting between many possible options, for example, as part of an efficient training process for random decision tree training, for selecting which of many families of models best describes data, for selecting which of many features best classifies items. In various examples samples of information about uncertain options are used to score the options. In various examples, confidence intervals are calculated for the scores and used to select one or more of the options. In examples, the scores of the options may be bounded difference statistics which change little as any sample is omitted from the calculation of the score. In an example, random decision tree training is made more efficient while retaining accuracy for applications not limited to human body pose detection from depth images.

Abstract: Resource allocation for machine learning is described such as for selecting between many possible options, for example, as part of an efficient training process for random decision tree training, for selecting which of many families of models best describes data, for selecting which of many features best classifies items. In various examples samples of information about uncertain options are used to score the options. In various examples, confidence intervals are calculated for the scores and used to select one or more of the options. In examples, the scores of the options may be bounded difference statistics which change little as any sample is omitted from the calculation of the score. In an example, random decision tree training is made more efficient whilst retaining accuracy for applications not limited to human body pose detection from depth images.