Density-sensitive semisupervised inference
密度敏感的半监督推断
Semisupervised methods are techniques for using labeled data $(X_{1},Y_{1}),\ldots,(X_{n},Y_{n})$ together with unlabeled data $X_{n+1},\ldots,X_{N}$ to make predictions. These methods invoke some assumptions that link the marginal distribution $P_{X}$ of $X$ to the regression function $f(x)$. For example, it is common to assume that $f$ is very smooth over high density regions of $P_{X}$. Many of the methods are ad-hoc and have been shown to work in specific examples but…