| Name : R-TransTGGM
| |
| Version : 1.0.0
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.6.4
| Date : 2024-06-14 11:51:43
|
| Group : Development/Libraries/Other
| Source RPM : R-TransTGGM-1.0.0-lp153.6.4.src.rpm
|
| Size : 4.05 MB
| |
| Packager : (none)
| |
| Summary : Transfer Learning for Tensor Graphical Models
|
Description :
Tensor Gaussian graphical models (GGMs) have important applications in
numerous areas, which can interpret conditional independence structures
within tensor data. Yet, the available tensor data in one single study
is often limited due to high acquisition costs. Although relevant
studies can provide additional data, it remains an open question how to
pool such heterogeneous data. This package implements a transfer
learning framework for tensor GGMs, which takes full advantage of
informative auxiliary domains even when non-informative auxiliary
domains are present, benefiting from the carefully designed
data-adaptive weights. Reference: Ren, M., Zhen Y., and Wang J. (2022).
\"Transfer learning for tensor graphical models\" < arXiv:2211.09391>.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
