A new multi-label similarity semantic learning(ML-SSL) model was proposed to solve the problem of label missing in existing multi-label image classification methods, It can produce better classification results by effectively recovering missing label information in training data. The model considers ...
这就造成了实际数据往往很难拥有它所应有的全部标签,这种数据就被称为“弱标签”(Weak-Label)数据。 近年来有许多基于“相似的实例具有相似的标签“假设的“弱标签学习”(Weak-Label Learning)方法[5-8]被提出来解决这种数据造成的预测效果下降。 不完整的多视图弱标签学习 显而易见,“不完整的多视图弱标签学习...
Multi-label: there exist many labels, and one instance may have more than one labels. Weak-label: multi-Label Learning With Missing Labels. Partial Multi-label: In practice, the complicated structure of the label space usually makes it hard to decide some hard labels are relevant or not. Pa...
In conventional multi-label learning, each training instance is associated with multiple available labels. Nevertheless, real-world objects usually exhibit
Multi-view multi-label (MVML) learning is a framework for solving the problem of associating a single instance with a set of class labels in the presence o
Missing label learning is an important branch of multi-label learning, which can handle incomplete labels with annotations. Previous work on multi-label learning with missing labels mainly considered data in a single view representation. Based on intuitive understanding, we propose a Two-step Multi-...
Propagation利用标记集合传递的直推式的多标记学习方法（3）WELL(Multi-Label Learning with Weak Label)...
论文笔记(二):Multi-Label Balancing with Selective Learning for Attribute Prediction,程序员大本营,技术文章内容聚合第一站。
Therefore, this study explores a deep learning-based approach to multi-label classification of software requirement smells, incorporating advanced neural network architectures such as LSTM, Bi-LSTM, and GRU with combined word embedding like ELMo and Word2Vec. We collected and prepared an 8120 ...
Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the ...