方法证据记录
Semi-supervised Support Vector Machine
Semi-supervised Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution, yielding better generalization when labeled samples are scarce.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Semi-supervised Support Vector Machine (S3VM / Transductive SVM)
分类方法记录 · ml-model / machine-learning
- Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209. · URL
- Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. · ISBN 978-0-262-03358-9
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