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The Cosine distance metric used in the proposed ensemble generaton does not capture the classi cation strategy of the SVM and is hence a poor metric for clustering images Due to the dif culty in obtaining good clusters of impostors, it is tough to obtain very diverse decisions on impostors The ROCs of 15 monotonic decision rules applied the three SVM classi ers in the proposed And rule ensemble are shown in Figure 419 The ROCs are obtained by nding a set of optimal thresholds on the three classi er scores for each decision fusion rule It is observed that the And rule is the best rule with an EER of 12% The performance of the monotonic fusion rules on the Bagging classi er ensemble is displayed in Figure 420 Here, the ROCs of many the decision fusion rules are comparable.

When compiled and executed, it generates the following output:

The output of this program is stored in a file named output Our next effort will be to define an input operator to read it back in Exercise 207 Given the following class Date definition,

class Date { public: // private: int month, day, year; };

The EERs of the Or rule and the Majority rule are 24% and 26% and hence are comparable Thus the better diversity on impostor decisions in the And rule ensemble have enabled a better fusion accuracy than the Bagging ensemble..

// spell the month out September 8th, 1997

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (982 / 1065) [2001-3-29 11:32:15]

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peer can allow for multiple trust anchors, one CERTREQ per trust anchor must be generated. The specification forbids sending certificate requests with an empty certificate authority field. The entity receiving a certificate request with X.509 certificate-signature as its certificate type must make sure that the certificate it is sending is acceptable to the requesting peer, i.e. it is not issued by a trust anchor different from the one specified in the CERTREQ. If not, the sending entity must include each certificate in the chain, from the sending entity s certificate to the certificate from the issuer whose name matches the Certificate Authority field within the CERTREQ.

with SVM classi ers on NIST 24 plastic distortion database. And fusion of three classi ers is the best decision fusion rule.

9 / 8 / 97

(c) Which, if either, is preferrable Why (d) Should the Date output operator be a friend function Why Why not Exercise 208 Define the output operator for the following CheckoutRecord class,

classi ers on NIST 24 plastic distortion database. Majority fusion and Or fusion of three classi ers are comparable.

class CheckoutRecord { public: // private: double book_id; string title; Date date_borrowed; Date date_due; pair<string,string> borrower; vector< pair<string,string>* > wait_list; };

Overloading the input operator (>>) is similar to overloading the output operator, except that the possibility of error is considerably greater For example, here is an implementation of the WordCount input operator:

4

#include <iostream> #include "WordCounth" /* must modify WordCount to specify input operator as friend: class WordCount { friend ostream& operator ( ostream&, const WordCount& ); friend istream& operator>>( istream&, WordCount& ); */ istream& operator>>( istream &is, WordCount &wd ) { /* format of WordCount object to be read: * <2> string * <7,3> <12,36> */ int ch; /* read in less-than token if not present * place istream in a fail state and exit */ if ((ch = isget()) != '<' ) {

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (983 / 1065) [2001-3-29 11:32:15]

9.3.2 Identity Management for ISAKMP: No IP Address, Please!

issetstate( ios_base::failbit ); return is; } // read in size value int occurs; is >> occurs; // grab >; not checking for error while ( is && (ch = isget()) != '>' ) ; is >> wd_word; // read in the locations; // format of each location: < line, col > for ( int ix = 0; ix < occurs; ++ix ) { int line, col; // extract values while (is && (ch = isget())!= '<' ) ; is >> line; while (is && (ch = isget())!= ',' ) ; is >> col; while (is && (ch = isget())!= '>' ) ; wdoccurListpush_back( Location( line, col )); } return is; }

Ensembles favorable to the Majority rule fusion are generated in this section The classi ers in the Majority rule ensemble are expected to make diverse decisions on both authentics and impostors Furthermore, the individual classi ers are expected to make a majority of correct decisions on both authentics and impostors Hence the classi er ensemble design for the Majority rule is tougher than the design for Or and And rules For the majority decision rule to be correct, more than half the classi ers should make a correct decision If the training data can be divided into N subsets, each classi er should be trained on a different set of N+1 subsets for an optimal coverage of the 2 training set This provides the most signi cant Majority decision fusion improvement over the individual classi er and the optimal diversity for the majority rule Table 4.

This example illustrates a number of issues with regard to possible iostream error states: 1 An istream that fails because of a incorrect format should mark the state of istream as fail:

issetstate( ios_base::failbit )

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