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An adaptive algorithm is an algorithm that changes its behavior at the time it is run, based on information available and on a priori defined reward mechanism (or criterion). Such information could be the story of recently received data, information on the available computational resources, or other run-time acquired (or a priori known) information related to the environment in which it operates. Among the most used adaptive algorithms is the Widrow-Hoff’s least mean squares (LMS), which represents a class of stochastic gradient-descent algorithms used in adaptive filtering and machine learning.
The Alaska class were six very large cruisers ordered before World War II for the United States Navy, of which only two were completed and saw service late in the war. The US Navy designation for the ships of this class was 'large cruiser' (CB) and the majority of leading reference works consider them as such. However, various other works have alternately described these ships as battlecruisers despite the US Navy having never classified them as such.
In computer programming and computer science, "maximal munch" or "longest match" is the principle that when creating some construct, as much of the available input as possible should be consumed. The earliest known use of this term is by R.G.G. Cattell in his PhD thesis on automatic derivation of code generators for compilers. For instance, the lexical syntax of many programming languages requires that tokens be built from the maximum possible number of characters from the input stream.