Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas.
Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）
Comparing K-Nearest Neighbors and Potential Energy Method in classification problem. A case study using KNN applet by E.M. Mirkes and real life benchmark data sets
Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios
Helmholtz Munich Technical University of Munich（海德堡-慕尼黑应用技术大学）；Ludwig-Maximilian-University Hospital University of Munich（路德维希-马克西米利安大学医院慕尼黑大学）；DKTK, German Cancer Consortium（德国癌症联合会）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Helmholtz Munich University of Fribourg（海德堡-慕尼黑应用技术大学弗里堡大学）
