Sparse distributed memory for multivalued patterns

Kanerva´s sparse distributed memory is developed for handling binary patterns. This algorithm is extended to be used with patterns of multivalued elements and applied to a gray-scale image recognition problem. Three different address activation methods are tested to identify the most useful methods. For data storage and retrieval, a method of minimal reading is proposed. Results concerning the recall accuracy of the three activation methods that work with multivalued data are discussed