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generative$31256$ - vertaling naar grieks

Generative Topographic Mapping; Generative topographic mapping; Generative Topographic Map

generative      
adj. γεννητικός
reproductive organs         
  • genitalia of Lepidoptera]]
  • An image of human male external sex organs (shaved [[pubic hair]])
  • An image of human female external sex organs (shaved [[pubic hair]])
ANY ANATOMICAL PART OF THE BODY INVOLVED IN SEXUAL REPRODUCTION AND CONSTITUTING THE REPRODUCTIVE SYSTEM IN A COMPLEX ORGANISM
Genitals; Genital; Genitalia; Primary sex characteristics; Genital organs; Sexual organ; Genital area; Male genitals; External genitalia; Internal genitalia; Sexual anatomy; Reproductive organ; Genital anatomy; Female genitals; Primary sex characteristic; Sexual organs; Reproductive organs; Male genital; Copulatory organ; Organs of generation; Genitalia, male; Genetalia; Male genitalia; Cock and balls; External genitals; Urinogenital; Female external genitalia; Male external genitalia; Male internal genitalia; Sex organs; External male genitalia; Males' genitalia; Female sex organ; Male sex organs; Male sex organ; Female sex organs; Sex anatomy; Mammalian genitalia; Genital organ; Male Genitals; Sex organs of mammals; Reproductive organs of mammals; Human sex organs; Urinogenitalia; Genital skin; Genital evolution; Evolution of sex organs; Evolution of genitalia; Generative organs
όργανα αναπαραγωγής

Definitie

generative
['d??n(?)r?t?v]
¦ adjective
1. relating to or capable of production or reproduction.
2. Linguistics relating to the application of a set of rules in order to produce all and only the well-formed items of a language.
Derivatives
generativity noun

Wikipedia

Generative topographic map

Generative topographic map (GTM) is a machine learning method that is a probabilistic counterpart of the self-organizing map (SOM), is probably convergent and does not require a shrinking neighborhood or a decreasing step size. It is a generative model: the data is assumed to arise by first probabilistically picking a point in a low-dimensional space, mapping the point to the observed high-dimensional input space (via a smooth function), then adding noise in that space. The parameters of the low-dimensional probability distribution, the smooth map and the noise are all learned from the training data using the expectation-maximization (EM) algorithm. GTM was introduced in 1996 in a paper by Christopher Bishop, Markus Svensen, and Christopher K. I. Williams.