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SEMI-SUPERVISED RANDOM DECISION FORESTS FOR MACHINE LEARNING

  • US 20130346346A1
  • Filed: 06/21/2012
  • Published: 12/26/2013
  • Est. Priority Date: 06/21/2012
  • Status: Active Grant
First Claim
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1. A machine learning process comprising:

  • accessing, using a processor, a plurality of labeled observations each labeled observation having a label indicating one of a plurality of classes that the labeled observation is a member of;

    accessing a plurality of unlabeled observations which are unlabeled in that, for each unlabeled observation, it is not known to which one of the plurality of classes the unlabeled observation belongs;

    training a plurality of random decision trees to form a semi-supervised random decision forest using both the labeled observations and the unlabeled observations such that each random decision tree partitions the labeled and the unlabeled observations into clusters according to similarity of the observations and according to the labels.

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