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descriptive set representation of features The choice of the current RIA is largely due to its recency and the simplicity in implementation Provided with sets of continuous feature values, the RIA is able to induce classi cation rules to partition the feature patterns into underlying categories There exists a number of approaches relevant to the rule induction task at hand, both from the point of view of applications and that of computational methods For example, the FAPACS (fuzzy automatic pattern analysis and classi cation system) algorithm documented in [12,50] is able to discover fuzzy association rules in relational databases It works by locating pairs of features that satisfy an interestingness measure that is de ned in terms of an adjusted difference between the observed and expected values of relations This algorithm is capable of expressing linguistically both the regularities and the exceptions discovered within the data.

When this script is called from a URL such as http://wwwdomaincom/cgi-bin/ calcpl num1=100&num2=300, it prints information in the standard error log for that site For the above-mentioned URL, the entry in the error log will similar to this:

The following statement prints this error message:

Figure C-13: Movetree.exe movetree {/start | /startnocheck | /continue | /check} /s SrcDSA /d DstDSA /sdn SrcDN /ddn DstDN [/u [Domain\]Username /p Password] [/verbose] [{/ | /help}]

DEBUG and print STDERR num1 is less than num2\n ;

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Modi cations to the Fuzzy ID3 (itself an augmentation of Quinlan s original ID3 [281]) rule induction algorithm have been documented [126] to better support fuzzy learning In a similar attempt [157] has proposed modi cations to decision trees to combine traditional symbolic decision trees with approximate reasoning, offered by fuzzy representation This approach rede nes the methodology for knowledge inference, resulting in a method best suited to relatively stationary problems A common disadvantage of these techniques is their sensitivity to high dimensionality This may be remedied using conventional work such as principal components analysis (PCA) [77, 100] As indicated previously, although ef cient, PCA irreversibly destroys the underlying semantics of the feature set Further reasoning about the derivation from transformed principal features is almost always humanly impossible In order to speed up the RIA and reduce rule complexity, a preprocessing step is required.

The interesting thing about this line is that it uses a constant called DEBUG, which is set in the beginning of the script with this line:

Parameters /start /startnocheck /continue /check Explanation Starts a Movetree operation. This command includes a /check operation by default. Starts a Movetree operation with no /check. Continues the execution of a previously paused or failed Movetree operation. Performs a test of the Movetree operation, checking the whole tree without moving any objects. An error message occurs if necessary permissions are not met, the destination server does not have enough disk space, an RDN conflict exists, a sameAccountName conflict occurs, or nonmovable objects exist (see previous bulleted list). Specifies the fully qualified primary DNS name of the source server in the domain from which the objects are being moved. Specifies the fully qualified primary DNS name of the destination server in the domain to which the objects are being moved. Specifies the distinguished name of the source subtree to which the object is being moved. Specifies the distinguished name of the destination subtree to which the object is being moved. Runs Movetree under the credentials of a valid user name and password. Runs Movetree in verbose mode. Displays the command-line syntax.

use constant DEBUG => 1;

This is particularly important for tasks where learned rulesets need regular updating to re ect the changes in the description of domain features This step reduces the dimensionality of potentially very large feature sets while minimizing the loss of information needed for rule induction It has an advantageous side effect in that it removes redundancy from the historical data This also helps simplify the design and implementation of the actual pattern classi er, by determining what features should be made available to the system In addition the reduced input dimensionality increases the processing speed of the classi er, leading to better response times Most signi cant, however, is the fact that fuzzy-rough feature selection (FRFS) preserves the semantics of the surviving features after removing any redundant ones.

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