HMTL: Heterogeneous Multi-Task Feature Learning
The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.
||R (≥ 3.5.0), stats, graphics, Matrix, pROC
||Yuan Zhong [aut, cre],
Wei Xu [aut],
Xin Gao [aut]
||Yuan Zhong <aqua.zhong at gmail.com>
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