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Paper Reading: Oversampling with Reliably Expanding Minority Class Regions for Imbalanced Data Learning

为了设计更有效的插值过采样算法，本文提出了一种新的插值过采样方法 OREM。OREM 在原始少数类样本周围找到候选少数类区域，然后利用这些候选区域识别不包含任何多数类样本的干净子区域。它们被认为是潜在的少数类区域，所以通过将合成样本填充到干净子区域可以增强少数类的表达能力。OREM 方法的思路很简单... ......
Oversampling Imbalanced Expanding Minority Learning

Expanding disk inside Hyper-V using LVM

based on https://askubuntu.com/questions/646183/expanding-disk-inside-hyper-v-using-lvm#:~:text=First%2C expand the virtual disk," then "Expand").&tex ......
Expanding Hyper-V inside Hyper using
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