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HMTL

Heterogeneous Multi-Task Feature Learning

v0.1.0 · May 4, 2023 · GPL-3

Description

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>.

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026

Dependency Network

Dependencies Reverse dependencies Matrix pROC HMTL

Version History

new 0.1.0 Mar 9, 2026