e/Cluster-weighted modeling

Information
has gloss	eng: In statistics, cluster-weighted modeling (CWM) is an algorithm-based approach to density estimation in joint input-output space proposed by Neil Gershenfeld. The base CWM algorithm gives a single output cluster for each input cluster. However, CWM can be extended to multiple clusters which are still associated with the same input cluster. Each cluster in CWM is localized to a Gaussian input region, and this contains its own trainable local model. It is recognized as a versatile inference algorithm which provides simplicity, generality, and flexibility; even when a feedforward layered network might be preferred, it is sometimes used as a "second opinion" on the nature of the training problem.
lexicalization	eng: Cluster-weighted modeling
instance of	c/Data clustering algorithms