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[58] pioneered the work of multiple kernel learning (MKL) in which the optimal kernel matrix is obtained as a linear combination of prespeci ed kernel matrices It was shown [58] that the coef cients in MKL can be determined by solving convex programs in the case of Support Vector Machines (SVM) While most existing work focuses on learning kernels for SVM, Fung et al [59] proposed to learn kernels for discriminant analysis Based on ideas from MKL, this problem was reformulated as a semide nite program (SDP) [60] in reference 61 for binary-class problems By optimizing an alternative criterion, an SDP formulation for the KDA kernel learning problem in the multiclass case was proposed in reference 62 To reduce the computational cost of the SDP formulation, an approximate scheme was also developed.

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// an overloaded set of min() functions // each must have a unique parameter list #include <string>; int min( const int *pia, int size ); int min( int, int ); int min( const char *str ); char min( string ); string min( string, string );

The run-time behavior of an overloaded function is the same as that of a nonoverloaded function The primary overhead is the time during compilation to determine which of the multiple instances to invoke Without support for function overloading, we would be required to provide a unique name for each function in our program (Function overloading is examined in detail in 9) We've identified the following three constructors for our IntArray class:

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Furthermore, it was shown that the regularization parameter for KDA can also be learned automatically in this framework [62] Although the approximate SDP formulation in reference 62 is scalable in terms of the number of classes, interior point algorithms [63] for solving SDP have an inherently large time complexity and thus it can not be applied to large-scale problems To improve the ef ciency of this formulation, a quadratically constrained quadratic program (QCQP) [63] formulation was proposed in reference 64 and it is more scalable than the SDP formulations..

class IntArray { public: explicit IntArray( int sz = DefaultArraySize ); IntArray( int *array, int array_size ); IntArray( const IntArray &rhs ); // private: static const int DefaultArraySize = 12; //

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7 [EAPKEYID], B Aboba et al, Extensible Authentication Protocol (EAP) Key Management Framework , IETF work in progress, Internet draft, draft-ietf-eap-keying-03txt, July 2004 8 [EAPIANA], type numbers for EAP specified by IANA, http://wwwianaorg/assignments/eap-numbers 9 [RADEXT2869], C Rigney et al, RADIUS Extensions , IETF, RFC 2869, June 2000 10 [RADEAP3579], B Aboba, P Calhoun, RADIUS (Remote Authentication Dial In User Service) Support for Extensible Authentication Protocol (EAP) , RFC 3579, September 2003 11 [EAPTLS2716], B Aboba, D Simon, PPP EAP TLS Authentication Protocol , IETF, RFC 2716, October 1999 12 [EAPTTLSDR], P Funk, S Blake-Wilson, EAP Tunneled TLS Authentication Protocol (EAP-TTLS) , IETF Internet draft, draft-ietf-pppext-ttls-05txt, April 2004 13 [PEAPDR], A Palekar et al, Protected EAP Protocol (Version 2) , IETF Internet draft, draft-josefsson-pppexteap-tls-eap-07txt, October 2004 14 [8021X2004], Standard for Local and Metropolitan Area Networks Port-Based Network Access Control , IEEE, Dec.

file:///F|/WinDDK/resources/CPPPrimer/c++primerhtm (40 / 1065) [2001-3-29 11:32:02]

turn off bit 27 This time the integer must have all the bits except the 27th turned on Notice that this is the inverse of the previous integer Applying the bitwise NOT to the previous integer will turn on every bit except the 27th:

Sparsity has recently received much attention for extending existing algorithms to induce sparse solutions [65 67]. L1 -norm penalty has been used in regression [68], known as LASSO, and SVM [69, 70] to achieve model sparsity. Sparsity often leads to easy interpretation and good generalization ability of the resulting model. Sparse Fisher LDA has been proposed in reference 35, for binary-class problems. Based on the equivalence relationship between LDA and MLR, a multiclass sparse LDA formulation was proposed in reference 71 and an entire solution path for LDA was also obtained through the LARS algorithm [72]. The discussions in this chapter focus on supervised approaches. In the unsupervised setting, LDA can be applied to nd the discriminant subspace for clustering, such as K-means clustering. In this case, an iterative algorithm can be derived alternating between clustering and discriminant subspace learning via LDA [73 75].

~( 1 27 );

If this value is bitwise ANDed with quiz1, all except the 27th bit will remain unchanged The 27th bit is turned off:

1

quiz1 &= ~(1 27);

Here is how the teacher can determine the on or off status of a bit Consider student 27 again (actually, her name is Anna) The first step is to set the 27th bit of an integer to 1 The bitwise AND of this value with quiz1 evaluates to true if bit 27 of quiz1 is also on; otherwise, it evaluates to false:

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