手法を比較

	自己教師ありOne-class 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	2018	2006 (book); roots in Kriging, 1951)
提唱者	Golan & El-Yaniv; Ruff et al.	Rasmussen, C. E. & Williams, C. K. I.
種類	Self-supervised anomaly/novelty detection	Probabilistic non-parametric model
原典	Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756.	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, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM	GP, Gaussian Process Regression, GPR, Kriging
関連	6	3
概要	Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples.	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.