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Fusion and propagation
| Глоссарий по искусственному интеллекту |
The fusion and propagation algorithm is a fundamental algorithm for producing multiple marginals from a graphical model that can be represented as a tree. it provides a rule for fusing incoming messages at each node and for propagating those messages out from a node. the fusion takes place in the local frame of discernment, so the full joint frame is never explicitly required. see also: graphical model, peeling.
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Propagation, английский
- Прохождение, распространение (напр, радиоволн)
- An act of causing something to spread or multiply
- Распространение; размножение; продвижение; передача
- Распространение
- The process of distributing an index from a content index server to one or more web servers for the purposes of providing search.
- Advancement of a wave through a medium.
- Advancement of energy or a crack through a medium. see also brittle crack propagation; ductile crack propagation; fatigue crack propagation.
- Movement of a wave through a medium.7,21
Fundamental, английский
- A основной; ко- ренной; базисный, главный, существенный frequency fusing a использующий фузию
- The lowest frequency in a complex sound wave.
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Fredkin prize, английский
The fredkin prize was $100,000 prize for the first computer program to beat a reigning world chess champion. mit computer science professor edward fredkin established the prize in 1980. the inventors of the deep blue chess machine won the fredkin prize. deep blue beat gary kasparov, the reigning world champ. professor fredkin offered the final $100,000 as the third in a series of three prizes. two scientists from bell laboratories (whose program first attained a masters rating in chess) won the first prize of $5,000. five carnegie mellon graduate students who built deep thought (the first program to achieve international master status) claimed the second prize of $10,000. see also: deep blue.
Fuzzy cozmplement, английский
The fuzzy complement of a fuzzy set is a fuzzy set whose membership functions are the complements of the original set (i.e., 1-m(a,x) where x are the elements in the original fuzzy set a).
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