Multi-label classification is an important learning problem with many applications. In this work, we propose a principled similarity-based approach for multi-label learning called SML. We also introduce a similarity-based approach for predicting the label set size. The experimental results demonstrate ...
Similarity-based classification framework is extensively used to address the problem of multi-label learning. Through this research, we establish the connection between similarity-based classification with many popular state-of-the-art multi-label learning models. In fact, we show that the similarity-...
Combining Instance-Based Learning and Logistic Regression for Multilabel Classification Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research... W Cheng,E Hüllermeier - Joint European Conference on ...
Wang, "Learning semantic similarity for multi-label text categorization," in Chinese Lexi- cal Semantics. CLSW 2014, X. Su and T. He, Eds., vol. 8922 of Lecture Notes in Computer Science, pp. 260-269, Springer, Cham, 2014.Li L, Wang M, Zhang L, et al.Learning semantic similarity ...
Our loss function re-weights the loss by computing the similarity and dissimilarity between positive samples and a given anchor based on the introduced relations. We mainly conduct experiments for multi-label text classification on MIMIC datasets, then further extend the evaluation on MS-COCO. The ...
CONNECTIVITY SIMILARITY BASED GRAPH LEARNING FOR INTERACTIVE MULTI-LABEL IMAGE SEGMENTATIONA system and method of connectivity-based image processing to identify and extract objects in image data. Variations on the method may include iterative local smoothing operations and various algorithmic solutions to ...
Curriculum learning can improve neural network training by guiding the optimization to desirable optima. We propose a novel curriculum learning approach for image classification that adapts the loss function by changing the label representation. The idea is to use a probability distribution over classes ...
In this paper, we propose a new similarity-based two-view multi-instance learning (STMIL) method that can incorporate two-view data into learning so as to improve classification accuracy of MIL. In order to obtain the predictive classifier, we first convert the proposed model into a convex ...
MLKNN_train and MLKNN_test functions can be found on Zhang Minling's homepage at https://palm.seu.edu.cn/zhangml/ (Resources, Multi-label lazy learning approach). citation @article{HE2024111948, title = {Multi-label feature selection via similarity constraints with non-negative matrix factoriz...
近期我们小组做了一项对比学习方面的工作:“Inter-Instance Similarity Modeling for Contrastive Learning”,主要用于解决现有对比学习方法在训练过程中忽略样本间相似关系,从而导致所学习无监督表征在不同样本之间的泛化能力下降问题。所提出的方法在ImageNet-1K、CIFAR10和CIFAR100上取得了显著的性能提升。该项工作在我们后...