Multiple-Instance Learning of Real-Valued Data
Daniel R. Dooly, Qi Zhang, Sally A. Goldman, Robert A. Amar;
3(Dec):651-678, 2002.
Abstract
The multiple-instance learning model has received much attention
recently with a primary application area being that of drug activity
prediction. Most prior work on multiple-instance learning has been for
concept learning, yet for drug activity
prediction, the label is a real-valued affinity measurement giving the
binding strength. We present extensions of
k-nearest neighbors (
k-NN), Citation-
kNN, and the diverse density algorithm for the real-valued
setting and study their performance on Boolean and
real-valued data. We also provide a method for generating chemically
realistic artificial data.
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