General Versions
The basic 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.
The original form proposed by Gershenfeld describes two innovations:
- Enabling CWM to work with continuous streams of data
- Addressing the problem of local minima encountered by the CWM parameter adjustment process
CWM can be used to classify media in printer applications, using at least two parameters to generate an output that has a joint dependency on the input parameters.
Read more about this topic: Cluster-weighted Modeling
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