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The particular sub eld of machine learning that is concerned with learning more complex knowledge from training data than can be represented by propositional logic is that of inductive logic programming (ILP) [241]. This type of learning is often termed relational learning as the learned description may specify relations among parts of an object. ILP is distinguished by the use of background knowledge (in addition to the use of concrete examples) that acts as a model or template for the solution that must be learned by the algorithm. This background knowledge therefore restricts the solution search space and makes the computation more tractable. ILP was originally focused on program synthesis in logic languages such as Prolog, but such programs may be considered rules. A consequent shift has occurred in research that also emphasizes the use of ILP methods for knowledge discovery in databases. Here a distinction is often made between predictive ILP, where the aim is the learning of classi cation and prediction rules, and descriptive ILP, where the aim is the learning of clausal theories and association rules. A more detailed discussion of ILP status, issues, and future trends can be found in [196]. For rst-order classi cation rule induction the examples in the training data are commonly described by ground facts, namely logical formulas that contain no variables and have exactly one predicate that is positive. The hypothesis language is frequently a restriction of Horn clauses, namely clauses containing at most one literal. Well-known nonfuzzy algorithms include FOIL [280], GOLEM [242], and PROGOL [240], which are mainly concerned with the learning of single predicates from positive and negative examples and background knowledge

Table 2-3 (continued)

Figure C-7: Netdiag.exe netdiag [/q] [/v] [/l] [/debug] [/d:DomainName] [/fix] [/DcAccountEnum] [/test:testname] [/skip:testname]

MPM winnt Limit Options HARD_SERVER_LIMIT Default Value 1 Notes C header (include) filename: %APACHE_SOURCE%/server/mpm/ winnt/mpm_defaulth #define HARD_SERVER_LIMIT 1 This setting should not be changed Change the hard thread count instead winnt HARD_THREAD_LIMIT 4096 C header (include) filename: APACHE_SOURCE/server/mpm/ winnt/mpm_defaulth #ifndef HARD_THREAD_LIMIT #define HARD_THREAD_LIMIT 4096 #endif

There are several different approaches for reasoning with imperfect or imprecise knowledge, including fuzzy rule-based systems that are based on fuzzy set theory and fuzzy logic [408]. At the core of a such a system are a knowledge base and an inference procedure:

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When changing HARD_SERVER_LIMIT or HARD_THREAD_LIMIT to higher than the default make sure you have the appropriate system resources For example, changing the HARD_SERVER_LIMIT to 1024 for the prefork MPM will allow you to create 1024 Apache child processes by instructing Apache to create that many children by using the directives StartServers, MinSpareServers, and MaxSpareServers However, if your system does not have enough memory, then changing the hard limit to a higher value will not do much good Remember that the higher the limits, the more resources you need You also need to increase the number of file descriptors your system allows for a single user On Linux and Unix systems you should find out what (and possibly set) the file descriptor limits are by using the ulimit command

Parameters /q /v /l /debug /d:DomainName Explanation Specifies quiet output (errors only). Specifies verbose output. Sends output to NetDiag.log; the log file is created in the same directory where netdiag.exe was run. Specifies even more verbose output; may take a few minutes to complete. Finds a domain controller in the specified domain.

Also note that the MaxClients directive sets the limit on the number of child processes that will be created to serve requests When the server is built without threading, no more than this number of clients can be served simultaneously To configure more than 256 clients, you must edit the HARD_SERVER_LIMIT entry in mpm_defaulth and recompile

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