Titlebook: Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering; Larisa Angstenberger Book 2001 Springer Science+Business M

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吝嗇性

General Framework of Dynamic Pattern Recognition,are a lot of applications in which the order of state changes of an object over time determines its membership to a certain pattern, or class. In these cases, for the correct recognition of objects it is very important not only to consider properties of objects at a certain moment in time but also t

過(guò)于平凡

Stages of the Dynamic Pattern Recognition Process, updating the classifier according to detected changes in the cluster structure. Different approaches used for establishing the monitoring process are usually based on the observation and the analysis of some characteristic values describing the performance of a classifier or the cluster structure.

6Applepolish

Dynamic Fuzzy Classifier Design with Point-Prototype Based Clustering Algorithms,ure. The main property of a dynamic classifier is its ability to recognise temporal changes in the cluster structure caused by new objects and to adapt its structure over time according to the detected changes. The design of a dynamic fuzzy classifier consists of three main components: monitoring pr

雪崩

Similarity Concepts for Dynamic Objects in Pattern Recognition,sters so that objects belonging to any one of the clusters would be as similar as possible and objects of different clusters as dissimilar as possible. The most important problem arising in this context is the choice of a relevant similarity measure, which is then used for the definition of the clus

discord

Applications of Dynamic Pattern Recognition Methods,nsidered in this chapter. The first example taken from credit industry and presented in Section 6.1 is concerned with the problem of bank customer segmentation based on customers’ behavioural data. After a description of the credit data of bank customers and the formulation of the goals of the analy

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Conclusions,ge number of applications where for a correct recognition of structure in data the consideration of the temporal development of objects over time is required, for instance state-dependent machine maintenance and diagnosis, the analysis of bank customers’ behaviour for the evaluation of their creditw

大猩猩

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https://doi.org/10.1007/978-3-540-47857-7adapted to temporal changes detected by the monitoring process. The updating strategies of a dynamic classifier presented in Section 3.2 depend on the type of temporal changes in the cluster structure (gradual or abrupt) and can require either the adjustment of classifier parameters or complete re-l

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