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X) and to speci c values that the variable assumes by a lower case latter (eg x) Evans et al (1993) have presented, for most of the commonly encountered distributions, a compendium of parameters and related functions, including many that are not discussed here Most of the material included in the following two sections and in Tables A1, A2 and A3 can be found in their book Some of the other material was taken from standard references such as Burington and May (1970), or independently derived by the authors A discrete variable can have n distinct values Each of these values has a probability of occurrence, which we will designate pi The collection of all the pi is called the probability mass function or pmf It is usually written as pX (x) or pi .

(17.5)

CompanyName LIKE #Trim(Arguments.CompanyFilter)#% </cfif> ORDER BY CompanyName </cfquery> <cfreturn companyRecs> </cffunction> <!--- CreateCompany() [class method] ---> <cffunction name= CreateCompany returntype= void > <cfargument name= CompanyName type= string > <cfargument name= Address type= string > <cfargument name= City type= string > <cfargument name= State type= string > <cfargument name= ZipCode type= string > <cfargument name= Comments type= string > <cfset Var insCompany = > <cfquery name= insCompany datasource= #Request.MainDSN# > INSERT INTO Company( CompanyName, Address, City, State, ZipCode, Comments ) VALUES ( #Trim(Arguments.CompanyName)# , #Trim(Arguments.Address)# , #Trim(Arguments.City)# , #Trim(Arguments.State)# , #Trim(Arguments.ZipCode)# , #Trim(Arguments.Comments)# ) </cfquery> <cfreturn> </cffunction> <!--- UpdateCompany() [class method] ---> <cffunction name= UpdateCompany returntype= void > <cfargument name= CompanyID type= numeric > <cfargument name= CompanyName type= string > <cfargument name= Address type= string > <cfargument name= City type= string > <cfargument name= State type= string >

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<cfargument name= ZipCode type= string > <cfargument name= Comments type= string > <cfset Var updCompany = > <cfquery name= updCompany datasource= #Request.MainDSN# > UPDATE Company SET CompanyName = #Trim(Arguments.CompanyName)# , Address = #Trim(Arguments.Address)# , City = #Trim(Arguments.City)# , State = #Trim(Arguments.State)# , ZipCode = #Trim(Arguments.ZipCode)# , Comments = #Trim(Arguments.Comments)# WHERE CompanyID = #Arguments.CompanyID# </cfquery> <cfreturn> </cffunction> <!--- DeleteCompany() [class method] ---> <cffunction name= DeleteCompany returntype= void > <cfargument name= CompanyID type= numeric > <cfset Var delCompany = > <cfquery name= delCompany datasource= #Request.MainDSN# > DELETE Company WHERE CompanyID = #Arguments.CompanyID# </cfquery> <cfreturn> </cffunction> <!--- SizeOfCompany() [class method] ---> <cffunction name= SizeOfCompany returntype= numeric > <cfargument name= CompanyID type= numeric > <cfset Var GetNumEmployees = > <cfquery name= GetNumEmployees datasource= #Request.MainDSN# > SELECT COUNT(*) AS NumEmployees FROM Employee

where vj is the jth eigenvector, w is the gallery or probe set member, and w is the projected trace. At this stage, every individual known to the system is represented by a projection of the gallery set. The fourth and nal step is to compute the distances between each heartbeat from the probe dataset to each enrolled individual in the gallery. First, the probe traces are projected into the eigenspace using Eq. (17.5). The distance between the projected gallery traces and the projected probe traces is computed using an appropriate distance metric. Speci cally, we computed the distances using the Mahalanobis cosine [Eq. (17.6)] based upon its exceptional performance with eigenFace [37]. sij = p g , |p|| | g where p xj = g j (17.6)

Listing 23-3 (continued)

WHERE CompanyID = #Val(Arguments.CompanyID)# </cfquery> <cfreturn GetNumEmployees.NumEmployees> </cffunction> </cfcomponent>

Obviously, since the n distinct values exhaust al the possibilities, n pi = 1 i=1 The probability that the variable has the ith value is pi , and the probability that the variable has a value equal to or less than the ith value is i =1 pj This is called the j cumulative distribution function or CDF, written FX (x) If the ith value of the variable is xi , we can de ne moments of the distribution, mk :.

and where p and g are the probe and gallery examples of interest. Each score for a given gallery example is normalized by the standard deviation of all scores. The normalization generates higher assignment performance than do nonnormalized values [34].

The new SizeOfCompany() method returns an integer representing the number of employees who work for a specified company. You must set the access type of the SizeOfCompany() method to package because it is called by the Employee component only, as you see later on in the section Building the Employee Component. You should already be familiar with the other component methods from 22, so we don t need to explain them here. The only things left to add to this component s methods are their access and output settings, and you also need to specify whether arguments are required. The finished Company component is shown in Listing 23-4.

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