The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning - Special issue on inductive transfer
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logistic regression with an auxiliary data source
ICML '05 Proceedings of the 22nd international conference on Machine learning
Constructing informative priors using transfer learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Multi-task learning for HIV therapy screening
Proceedings of the 25th international conference on Machine learning
Convex multi-task feature learning
Machine Learning
Partially supervised feature selection with regularized linear models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
IEEE Transactions on Neural Networks
Protein crystallization prediction with AdaBoost
International Journal of Data Mining and Bioinformatics
International Journal of Data Mining and Bioinformatics
Hi-index | 0.00 |
Leveraging information from the publicly accessible data repositories can be very useful when training a classifier from a small-sample microarray data. To achieve this, we proposed a multi-task feature selection filter that borrows strength from auxiliary microarray data. It uses Kruskal Wallis test on auxiliary data and ranks genes based on their aggregated p-values. The top-ranked genes are selected as features for the target task classifier. The multi-task filter was evaluated on microarray data related to nine different types of cancers. The results showed that the multi-task feature selection is very successful when applied in conjunction with both single-task and multi-task classifiers.