A self-organizing, distributed, massively parallel network anatomy for the recognition of input stimuli and the learning of temporal patterns is proposed. The network adapts itself to recognize individual incoming events in the first, or static, subsystem. These recognized events, received by the sy
✦ LIBER ✦
Pattern recognition with evidential knowledge
✍ Scribed by M. A. Vila; M. Delgado; A. F. Gómez-Skarmeta
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 161 KB
- Volume
- 14
- Category
- Article
- ISSN
- 0884-8173
No coin nor oath required. For personal study only.
✦ Synopsis
Many real situations may be seen as classifications problems if the information used to Ž . Ž relate items with classes is a probability measure on P P X = C set of all subsets of . Ž . X = C . It is well known that a probability measure on P P X = C induces two evidence measures on X = C. We will formulate a classification model where it is assumed classes are fuzzy sets and the available information is evidential. Two bad classification loss functions will be established for this purpose, by using Dempster's upper and lower integrals.
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