A practical Bayesian framework for backpropagation networks
Neural Computation
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
Multiple-Instance Learning Improves CAD Detection of Masses in Digital Mammography
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Supervised learning from multiple experts: whom to trust when everyone lies a bit
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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This paper deals with learning spiculation scores of masses in a supervised manner Three spiculation score prediction models treating the score either as a continuous or ordinary variable are presented These models were compared on a data-set of 255 masses.