Machine Learning - Special issue on inductive transfer
Empirical Bayes for Learning to Learn
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Convergence of alternating optimization
Neural, Parallel & Scientific Computations
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
| Hi-index | 0.00 |
A mathematical programming formulation is proposed to eliminate irrelevant and redundant features for collaborative computer aided diagnosis which requires to detect multiple clinically-related malignant structures from medical images. A probabilistic interpretation is described to justify our formulations. The proposed formulation is optimized through an effective alternating optimization algorithm that is easy to implement and relatively fast to solve. This collaborative prediction approach has been implemented and validated on the automatic detection of solid lung nodules by jointly detecting ground glass opacities.