Multiple-Instance Learning of Real-Valued Data
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.
Cite
Text
Dooly et al. "Multiple-Instance Learning of Real-Valued Data." Journal of Machine Learning Research, 2002.Markdown
[Dooly et al. "Multiple-Instance Learning of Real-Valued Data." Journal of Machine Learning Research, 2002.](https://mlanthology.org/jmlr/2002/dooly2002jmlr-multipleinstance/)BibTeX
@article{dooly2002jmlr-multipleinstance,
title = {{Multiple-Instance Learning of Real-Valued Data}},
author = {Dooly, Daniel R. and Zhang, Qi and Goldman, Sally A. and Amar, Robert A.},
journal = {Journal of Machine Learning Research},
year = {2002},
pages = {651-678},
volume = {3},
url = {https://mlanthology.org/jmlr/2002/dooly2002jmlr-multipleinstance/}
}