EM-DD: An Improved Multiple-Instance Learning Technique

  title={EM-DD: An Improved Multiple-Instance Learning Technique},
  author={Qi Zhang and Sally A. Goldman},
We present a new multiple-instance (MI) learning technique (EMDD) that combines EM with the diverse density (DD) algorithm. EM-DD is a general-purpose MI algorithm that can be applied with boolean or real-value labels and makes real-value predictions. On the boolean Musk benchmarks, the EM-DD algorithm without any tuning significantly outperforms all previous algorithms. EM-DD is relatively insensitive to the number of relevant attributes in the data set and scales up well to large bag sizes… CONTINUE READING
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