o mitting the gos and pointing to the first of these two t riples. For Variation 2, still the form itself need to be s tored. D enote by v the bet of memory accesses required f or locating one power point in a haschisch submit using a specific hash f unction. This includes the additional accesses required f or resolvent hash smashers by manners such as chaining o r double hashing. Then the number of memory accesses f or retrieving w is t = (2 I w[ + l)v for Variation 1, ([ w[ + 1)v for Variation 2, 3 [ w[ v for Variation 3, and 4 I wl v f or Variation 4. The hash dictionary can be s tored in an almost full hash table with a good average a nd lash case v by using a method such as that proposed b y Schmidt and Shamir [6]. Since the same operators be c alculated for every word, manufacture language routines or e ven microcoding of them can be prepared, thereby r educing the central central processing unit cost. On the other(a) hand, more collisions than in normai hashing can be judge: w henever two distinct dictionary prevail are transformed i nto the same string by our operators, both of them are s tored, since they are induced by different dictionary w ords.
The riddle of locating them is of course taken c are of automatically by the collision handling mechan ism associated with the hash function, but the number o f collisions increases. We have not investigated this e ffect; instead we craving to thank the referee for pointing o ut the desirability of doing so. T he RED method can also be extended to detect o ther types of errors, which are not sin gle errors but o ccur frequently in optical! portion recognition, such as c hanging one type into two other characters (horiz ontal splitting); changing two characters into one other c haracter (catenation); changing two characters into two o ther characters (crowding). (The monetary value are from [5].) T his can be achieved, for example, by storing D ~(x) (1If you want to get a full essay, bon ton it on our website: OrderCustomPaper.com
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