Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

Online reviews are often the primary factor in a customer's decision to purchase a product or
service, and are a valuable source of information that can be used to determine public …

文本自动分类是信息检索与数据挖掘领域的研究热点与核心技术, 近年来得到了广泛的关注和
快速的发展. 提出了基于机器学习的文本分类技术所面临的互联网内容信息处理等复杂应用的 …

Learning from positive and unlabeled data or PU learning is the setting where a learner only
has access to positive examples and unlabeled data. The assumption is that the unlabeled …

From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU
learning, in which the state of the art is unbiased PU learning. However, if its model is very …

Learning with noisy labels is a common challenge in supervised learning. Existing
approaches often require practitioners to specify noise rates, ie, a set of parameters …

Plant‐pathogenic fungi secrete effector proteins to facilitate infection. We describe extensive
improvements to EffectorP, the first machine learning classifier for fungal effector prediction …

We study the problem of learning conditional generators from noisy labeled samples, where
the labels are corrupted by random noise. A standard training of conditional GANs will not …

Urban land use information plays an essential role in a wide variety of urban planning and
environmental monitoring processes. During the past few decades, with the rapid …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …