Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleFebruary 2024
- research-articleJanuary 2024
Three-stage Transferable and Generative Crowdsourced Comment Integration Framework Based on Zero- and Few-shot Learning with Domain Distribution Alignment
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 3Article No.: 68, Pages 1–43https://doi.org/10.1145/3636511Online shopping has become a crucial way to encourage daily consumption, where the User-generated, or crowdsourced product comments, can offer a broad range of feedback on e-commerce products. As a result, integrating critical opinions or major attitudes ...
- editorialOctober 2023
- research-articleSeptember 2023
WF-Transformer: Learning Temporal Features for Accurate Anonymous Traffic Identification by Using Transformer Networks
IEEE Transactions on Information Forensics and Security (TIFS), Volume 192024, Pages 30–43https://doi.org/10.1109/TIFS.2023.3318966Website Fingerprinting (WF) is a network traffic mining technique for anonymous traffic identification, which enables a local adversary to identify the target website that an anonymous network user is browsing. WF attacks based on deep convolutional ...
- ArticleDecember 2012
Interval-Valued Centroids in K-Means Algorithms
ICMLA '12: Proceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 01December 2012, Pages 478–481https://doi.org/10.1109/ICMLA.2012.87The K-Means algorithms are fundamental in machine learning and data mining. In this study, we investigate interval-valued rather than commonly used point-valued centroids in the K-Means algorithm. Using a proposed interval peak method to select initial ...
-
- ArticleJanuary 2012
Studying Active Learning in the Cost-Sensitive Framework
HICSS '12: Proceedings of the 2012 45th Hawaii International Conference on System SciencesJanuary 2012, Pages 1097–1106https://doi.org/10.1109/HICSS.2012.552Active learning is a learning paradigm that actively acquires extra information with an "effort" for a certain "gain" when building learning models. This paper unifies the effort and gain by studying active learning in the cost-sensitive framework. The ...
- ArticleDecember 2011
Simple Multiple Noisy Label Utilization Strategies
ICDM '11: Proceedings of the 2011 IEEE 11th International Conference on Data MiningDecember 2011, Pages 635–644https://doi.org/10.1109/ICDM.2011.133With the outsourcing of small tasks becoming easier, it is possible to obtain non-expert/imperfect labels at low cost. With low-cost imperfect labeling, it is straightforward to collect multiple labels for the same data items. This paper addresses the ...