Example sentences of "the [noun] [verb] " in BNC.

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1 The fingerboard/neck jointing and overall fretwork is exemplary ; the neck-edging feels impossibly smooth , and hopefully manufacturing consistency means that all similar-spec 'd Bass Collection instruments will be as good as this .
2 Once the interlining has been locked in , the two layers of fabric can be treated as one and made up in the same way .
3 Once it has recorded features of the sequence of parses , the algorithm continues :
4 He reports that the algorithm does indeed usually converge .
5 We agree that the specific algorithm we used wold have been inappropriate if we were interested in examining seasonal or short-term changes in primary production , not because the algorithm does not include a grazing term but because it does not include terms for irradiance and quantum efficiency .
6 This is not surprising as the algorithm to assign risk is weighted in favour of such cases .
7 A node and its children may all have almost identical f values , so the algorithm lacks sensible guidance .
8 The algorithm passes credit between rules , similarly .
9 Records will be read into primary storage , their keys will be transformed by the algorithm being used , and if the address the algorithm produces is free they will be stored in that address on the direct access device .
10 The algorithm keeps a second list , called CLOSED , of nodes which have been expanded and removed from OPEN .
11 As well as the set OPEN of known nodes which might lead to goals , and whose children have yet to be explored , the algorithm keeps a set called CLOSED of other known nodes whose children have been constructed .
12 The algorithm keeps a set of several strings .
13 The number of strings which the algorithm keeps may range up to a few thousand , and each bit string may be a few hundred long .
14 The algorithm calculates diff recursively for nodes in successive layers , starting at the top output layer and working back down to the input layer — hence its name , back propagation .
15 In each learning cycle , the algorithm calculates H , for each of its strings .
16 In one cycle , the algorithm takes one operator out of the list LOp and applies it to N , so producing just one child .
17 The algorithm takes about 50 CPU seconds on a SUN SPARCStation II to order 180 probes and 1150 clones from the S.pombe YAC library , and 247 CPU seconds to order 667 probes and 2837 clones from the S.pombe cosmid library , including the phase of contig ordering and consistency checking .
18 The algorithm involves two numbers , K and c .
19 The algorithm implemented with this particular robot could re-learn after its TV cameras were jogged .
20 Extend the algorithm to deal with a general dividend and divisor , and include a test for overflow ( i.e. when the quotient can not be represented in n bits ) .
21 Maybe P and Q and R behave alike , but there may be times when the algorithm folds a set { P , Q , R } where , say , Q sometimes occurs in a context which never contains P or R. The algorithm looks for any context which contains some of X 's children but not the others .
22 Whichever path the algorithm took , that path would pick up shortfall or cost of .1 .
23 It can not be exactly the algorithm given above because the desired outputs d from the top layer are not known , so it can not calculate y * ( 1-y ) * ( d-y ) .
24 The algorithm chooses for expansion the node having the best actual score so far , together with the best estimated score , h* ( n ) .
25 The algorithm discussed here omits the start symbol . )
26 His analysis was more like the algorithm embodied in one of the later versions of Bacon .
27 The algorithm runs thus : IF Vs>Vdb
28 The algorithm runs thus .
29 It keeps a list , called OPEN , of nodes which the algorithm has found and which are not goals but whose children might be goals .
30 The algorithm has a new variable , S , whose value is such a pair .
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