50 citations · 201 across the 20 of their papers we have counts for
6 papers · 1 filter
Incremental Concept Learning via Online Generative Memory Recall
Huaiyu Li, Weiming Dong, Bao-Gang Hu
The ability to learn more and more concepts over time from incrementally arriving data is essential for the development of a life-long learning system. However, deep neural network…
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Huaiyu Li, Weiming Dong, Xing Mei +3
In this work, we propose a novel meta-learning approach for few-shot classification, which learns transferable prior knowledge across tasks and directly produces network parameters…
Self-Paced Learning: an Implicit Regularization Perspective
Yanbo Fan, Ran He, Jian Liang +1
Self-paced learning (SPL) mimics the cognitive mechanism of humans and animals that gradually learns from easy to hard samples. One key issue in SPL is to obtain better weighting s…
A study on cost behaviors of binary classification measures in class-imbalanced problems
Bao-Gang Hu, Wei-Ming Dong
This work investigates into cost behaviors of binary classification measures in a background of class-imbalanced problems. Twelve performance measures are studied, such as F measur…
A New Strategy of Cost-Free Learning in the Class Imbalance Problem
Xiaowan Zhang, Bao-Gang Hu
In this work, we define cost-free learning (CFL) formally in comparison with cost-sensitive learning (CSL). The main difference between them is that a CFL approach seeks optimal cl…
Derivations of Normalized Mutual Information in Binary Classifications
Yong Wang, Bao-Gang Hu
This correspondence studies the basic problem of classifications - how to evaluate different classifiers. Although the conventional performance indexes, such as accuracy, are commo…