Adapting RBF Neural Networks to Multi-Instance Learning

@article{Zhang2005AdaptingRN,
  title={Adapting RBF Neural Networks to Multi-Instance Learning},
  author={Min-Ling Zhang and Zhi-Hua Zhou},
  journal={Neural Processing Letters},
  year={2005},
  volume={23},
  pages={1-26}
}
In multi-instance learning, the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. A bag is positive if it contains at least one positive instance, while it is negative if it contains no positive instance. In this paper, a neural network based multi-instance learning algorithm named RBF-MIP is presented, which is derived from the popular radial basis function (RBF) methods… CONTINUE READING
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