手法を比較

	半教師ありワンクラスSVM	ガウス過程
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2001–2004	2006 (book); roots in Kriging, 1951)
提唱者	Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010	Rasmussen, C. E. & Williams, C. K. I.
種類	Semi-supervised anomaly / novelty detection	Probabilistic non-parametric model
原典	Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
別名	SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM	GP, Gaussian Process Regression, GPR, Kriging
関連	5	3
概要	Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline.	A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.