A specific example of the broader class of nondistributed representations. A representation where the input is associated with a single symbol or category. Thus, the number of symbols in the dictionary must be the same as the number of feature detectors, number of different configurations of representation space, and number of regions in input space. 

University of Michigan - Ann Arbor

Representations that may contain many entries but without significant meaningful separate control over each entry. Many learning algorithms are based on such representations, such as:
- Clustering methods
- K-nearest neighbors algorithms
- Decision trees
- Gaussian mixtures and mixtures of experts
- Kernel machines with a Gaussian (or other similarly local) kernel 
- Language or translation models based on n-grams

Nondistributed Representations

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

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