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Attribute msNPCallingStationID msNPSavedCallingStationID msRADIUSCallbackNumber msRADIUSFramedIPAddress msRADIUSFramedRoute msRADIUSServiceType msRASSavedCallbackNumber msRASSavedFramedIPAddress msRASSavedFramedRoute msRRASAttribute msRRASVendorAttributeEntry mS-SQL-Alias mS-SQL-AllowAnonymousSubscription mS-SQL-AllowImmediateUpdatingSu... mS-SQL-AllowKnownPullSubcription mS-SQL-AllowQueuedUpdatingSubs... mS-SQL-AllowSnapshotFilesFTPDow... mS-SQL-AppleTalk mS-SQL-Applications mS-SQL-Build mS-SQL-CharacterSet mS-SQL-Clustered mS-SQL-ConnectionURL mS-SQL-Contact mS-SQL-CreationDate mS-SQL-Database mS-SQL-Description mS-SQL-GPSHeight mS-SQL-GPSLatitude mS-SQL-GPSLongitude mS-SQL-InformationDirectory Syntax IA5-String IA5-String IA5-String Integer IA5-String Integer IA5-String Integer IA5-String Unicode String Unicode String Unicode String Boolean Boolean Boolean Boolean Boolean Unicode String Unicode String Integer Integer Boolean Unicode String Unicode String Unicode String Unicode String Unicode String Unicode String Unicode String Unicode String Boolean

Timeout sets the server timeout in seconds The default should be left alone The next three directives KeepAlive, MaxKeepAliveRequests, and KeepAliveTimeout are used to control the keep-alive behavior of the server You

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fuzzy similarity relations The rst employs the new similarity-based fuzzy lower approximation to locate subsets The second uses boundary region information to guide search Finally, a fuzzy extension to crisp discernibility matrices is given in order to discover fuzzy-rough subsets The methods are evaluated and compared using benchmark data 10: Further Advanced FS Methods This chapter introduces two promising areas in feature selection The rst, feature grouping, is developed from recent work in the literature where groups of features are selected simultaneously By reasoning with fuzzy labels, the search process can be made more intelligent allowing various search strategies to be employed The second, ant-based feature selection, seeks to address the nontrivial issue of nding the smallest optimal feature subsets This approach to feature selection uses arti cial ants and pheromone trails in the search for the best subsets.

Apache will use one of the next three <IfModule > containers based on which MPM you chose For example, if you configured Apache using the default MPM mode (threaded) on a Linux system, then the following <IfModule > container will be used:

Attribute mS-SQL-InformationURL mS-SQL-Keywords mS-SQL-Language mS-SQL-LastBackupDate mS-SQL-LastDiagnosticDate mS-SQL-LastUpdatedDate mS-SQL-Location mS-SQL-Memory mS-SQL-MultiProtocol mS-SQL-Name mS-SQL-NamedPipe mS-SQL-PublicationURL mS-SQL-Publisher mS-SQL-RegisteredOwner mS-SQL-ServiceAccount mS-SQL-Size mS-SQL-SortOrder mS-SQL-SPX mS-SQL-Status mS-SQL-TCPIP mS-SQL-ThirdParty mS-SQL-Type mS-SQL-UnicodeSortOrder mS-SQL-Version mS-SQL-Vines mustContain name nameServiceFlags nCName nETBIOSName netbootAllowNewClients

<IfModule threadedc> StartServers 3 MaxClients 8 MinSpareThreads 5 MaxSpareThreads 10 ThreadsPerChild 25 MaxRequestsPerChild 0 </IfModule>

Both of these developments can be applied within feature selection, in general, but are applied to the speci c problem of subset search within FRFS in this book 11: Applications II: Web Content Categorization With the explosive growth of information on the Web, there is an abundance of information that must be dealt with effectively and ef ciently This area, in particular, deserves the attention of feature selection due to the increasing demand for high-performance intelligent Internet applications This motivates the application of FRFS to the automatic categorization of user bookmarks/favorites and Web pages The results show that FRFS signi cantly reduces data dimensionality by several orders of magnitude with little resulting loss in classi cation accuracy 12: Applications III: Complex Systems Monitoring.

On the other hand, if you chose --with-mpm=prefork during source configuration by using the configure script, then the following <IfModule > container will be used:

<IfModule preforkc> StartServers 5 MinSpareServers 5 MaxSpareServers 10 MaxClients 20 MaxRequestsPerChild 0 </IfModule>

Syntax Unicode String Unicode String Unicode String Unicode String Unicode String Unicode String Unicode String Large Integer Unicode String Unicode String Unicode String Unicode String Unicode String Unicode String Unicode String Large Integer Unicode String Unicode String Large Integer Unicode String Boolean Unicode String Integer Unicode String Unicode String Object Identifier Unicode String Integer Distinguished Name Unicode String Boolean

If you followed my advice in the previous chapter, you did not change the default MPM behavior during source compilation and used the threaded behavior, so the directives that you need to consider are discussed below

Complex application problems, such as reliable monitoring and diagnosis of industrial plants, are likely to present large numbers of features, many of which will be redundant for the task at hand With the use of FRFS, these extraneous features can be removed This not only makes resultant rulesets generated from such data much more concise and readable but can reduce the expense due to the monitoring of redundant features The monitoring system is applied to water treatment plant data, producing better classi cation accuracies than those resulting from the full feature set and several other reduction methods 13: Applications IV: Algae Population Estimation Biologists need to identify and isolate the chemical parameters of rapid algae population uctuations in order to limit their detrimental effect on the environment.

If you did change the default MPM, you can find detailed information on the directives needed by your chosen MPM in 4

Table F-2 (continued)

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