Bandingkan kaedah

	Gaussian Process Separuh Selia	Mesin Vektor Sokongan Separa-Seliaan
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2004	1999
Pengasas	Lawrence, N. D. & Jordan, M. I.	Joachims, T.
Jenis	Probabilistic model (semi-supervised)	Semi-supervised classifier
Sumber perintis	Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press.	Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209.
Alias	SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning	S3VM, Transductive SVM, TSVM, Semi-SVM
Berkaitan	5	4
Ringkasan	Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive.	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.