Adaptive machine learning
Adaptive machine learning is a theory in computational adaptive machine learning for use in systems engineering. The limit of the ratio of the supplied machine need log likelihood and the adaptive need log likelihood goes to one as the parameter-space approaches the mean of both likelihoods. Systems engineering is defined as existing in two scales primary and secondary regions on the plot of each parameter as a function of life cycle. Life cycle is broken up into product, production, support, and retirement each projection on life cycle equates to a length. Four curves exist on the parameter projection of each point on the map of a stasis system each of which are functions of time or life cycle ease of change, knowledge, commitment, and cost. Stasis engineering is the systematic development of a map of stasis points in order to optimize both needs supplied and adaptive. The importance of stasis engineering in the development life cycle is that adaptive machine learning takes into account the special needs, disabilities, and ailments of people in both the developer and user end of the product lifecycle.
 | This systems-related article is a stub. You can help EverybodyWiki by expanding it. |
This article "Adaptive machine learning" is from Wikipedia. The list of its authors can be seen in its historical. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.
