Density Estimation with Equiprobabilistic Maps
نویسندگان
چکیده
Topographic maps have become standard tools for frequently encountered dataanalytic tasks such as visualization, clustering and classification. Unfortunately, however, a complete SOM training methodology is not firmly established yet. For example, when using the standard SOM fitting algorithm, it is well-known that an appropriate choice for the end adaptation radius or final neighborhood width is crucial for obtaining useful results. This parameter controls in effect the degree of smoothness (or the amount of quantization error minimization) in the final map. Several heuristics have been suggested to guide this choice. A new monitorization idea is introduced in this paper, and a few examples are used to illustrate the scope of the approach.
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تاریخ انتشار 2006