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var introduction:String = "My name is Roger; but I ll have you know MY name is Henry!"; trace(introduction.replace(/my name is (\w+)/gi, "$1 is my name")); //Roger is my name; but I ll have you know Henry is my name!

m-' (mod n ) = 41-l (mod 491) = 12.

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You have to search for the whole pattern my name is because you ll be modifying that phrase. However, of particular interest is the name itself, so you capture that. By using variables named $1, $2, and so on up to $99 in the replacement string, the rst, second, and ninety-ninth captured groups are resubstituted into the text that replaces the match. To make replacing text even sweeter, you can pass replace() a replacement function instead of a replacement string. The function is passed the matching text, all the captured groups for that match, and the full string in context, and you can make whatever kind of replacement you want. For instance, you could make sure that my friends names always appear capitalized properly.

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var friends:RegExp = /aashoo|betty|cesar|deepa|eve|frej|gina|hilary|isabell/gi; var fixCaps:Function = function fixCaps(...args):String { var match:String = String(args[0]); return match.charAt(0).toUpperCase() + match.substr(1).toLowerCase(); } var txt:String = "DEEPA is in SF cESaR is in Bogota and isaBELL is in Berlin."; trace(txt.replace(friends, fixCaps)); //Deepa is in SF Cesar is in Bogota and Isabell is in Berlin.

Now; suppose that Bob wants t o encrypt the message M = 150 for Alice. He first converts 150 t o binary, that is 10010110. He then uses the 1 bits t o select the elements of T that are summed t o give the ciphertext. In this example, Bob computes the ciphertext

The basic idea of Reduced-Dimensional SVM (RDSVM) to reduce the computational burden is that the total number of computations of SVM can be reduced by reducing the number of computations in kernel functions, since the number of observation vectors N cannot be reduced to a very low level in many cases. An effective way of reducing the number of computations in kernel functions is to reduce the dimensionality of observation vectors. RDSVM is in fact a combination of feature extraction and SVM algorithms. It has a two-layer structure. The first layer conducts feature extraction and compression, of which the objective is to reduce the dimensionality of the feature space and obtain the largest discriminants between classes. The second layer conducts SVM training in the reduced-dimensional feature space, which is provided by the first layer. Thus, the kernel functions will be calculated as follows: kx y = x y = TT x TT y = k TT x TT y (16.38)

You can do all kinds of things with custom substitutions, such as encoding particularly secret bits of information in an otherwise innocuous stream of text.

You can use regular expressions to break up text. This is useful when you are trying to interpret text that uses a delimiter to denote breaks between data.

C = 82

This method was used with String delimiters in 6 to split text into its constituents. The delimiter parameter, however, may also be a regular expression. You can use this to allow for more exibility in the delimiter. In this example, a haiku is split into phrases with slashes. However, you can t be entirely sure if the author will include spaces around the slashes, so you allow for whitespace on either side by splitting on a regular expression, as shown in Example 12-6.

EXAMPLE 12-6

Cm-l (mod n ) = 548.12 (mod 491) = 193.

Normal MCE training process Input General searching T process for the starting point Thorough MCE training Output

kernel MultiplyBlendMode <...> { input image4 source1; input image4 source2; output pixel4 result; void evaluatePixel() { result = sample(source1, outCoord()) * sample(source2, outCoord()); } }

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