Adapting RBF Neural Networks to Multi-Instance Learning

  title={Adapting RBF Neural Networks to Multi-Instance Learning},
  author={Min-Ling Zhang and Zhi-Hua Zhou},
  journal={Neural Processing Letters},
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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Publications referenced by this paper.
Showing 1-10 of 40 references

Neural Networks for Pattern Recognition

C. M. Bishop
View 6 Excerpts
Highly Influenced

Principle Component Analysis

I. T. Jollife
New York: Springer-Verlag, • 1986
View 6 Excerpts
Highly Influenced

Neural networks for multi-instance learning

Zhou, Z.-H, Zhang, M.-L
Technical Report, • 2002
View 11 Excerpts
Highly Influenced

Learning single and multiple decision trees for security applications

G. Ruffo
PhD dissertation, • 2000
View 5 Excerpts
Highly Influenced

Learning internal representations by error propagation

D. E. Rumelhart, G. E. Hinton, R. J. Williams
Parallel Distributed Processing: explorations in the microstructure of cognition, • 1986
View 5 Excerpts
Highly Influenced

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