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In the preceding, s/XYZCorp/ABCCorp/g is a command to sed itself. It says substitute XYZCorp with ABCCorp and do so globally, that is, for all instances of XYZCorp throughout the document. sed is a complex program, and we can t delve into it in this book. If you d like to learn more, you can read its manual page, look at a Web site such as you can find at www.cornerstonemag.com/sed/, or read any of the several books that have been written about it.

(The call to substr() is used to return just the last four digits, because the other octal digits in the returned value aren t relevant.)

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solved, if an algorithm does it also and often better That is, in such a case a normative approach is preferred to a descriptive approach Second, a cognitive task analysis means much work and requires special skills of software designers or knowledge engineers It always remains the question whether the extensive analysis was deep enough Knowledge engineers never know this for sure Third, the problem is that the analysis of a large number of users brings about a very diverse repertoire of solutions As a consequence, a knowledge engineer may end up with as many supporting systems as there are users If this is the ultimate outcome of the dedicated user-orientation approach, it is a threatening phantom for software designers The empirical study for three different domains discussed in Section 43 is meant to partly deal with these drawbacks.

After you ve uploaded the file to your web server, you can validate the file. You can see that all I did was point Feed Validator at the URL to the location of the uploaded file. This validation process guarantees that you have a feed that can be connected to and aggregated without causing any issues with the software reading your feed. If Feed Validator could not validate this feed, it would have pointed out the line and area that was invalid by highlighting the invalid code.

The replacement of the additive inverse of the Hessian matrix by the Fisher information matrix may have consequences for the optimization process The main reason is that, by definition, the Fisher information matrix is a covariance matrix See Section 441 Therefore, it is positive semidefinite Since, in addition, the use of the inverse of Ft implies that it is supposed nonsingular, it is positive definite and so is F;' Then, (667) if st # o at t = t, Therefore, the direction of A t F S is an ascent direction Since such a statementcannot always be made with respect to the direction of the Newton step, one might conclude that this property of the Fisher scoring method is an advantage over the Newton method.

However, if at a particular point the Hessian matrix is not negative definite, the Fisher scoring method nevertheless replaces it by a negative definite matrix This implies that the Fisher scoring method approximates the function locally by a negative definite quadratic form while the true quadratic approximation is not negative definite Then, the sense of such an approximation may be seriously doubted and, strictly, there is no reason to prefer at such points the Fisher scoring step to any other step in an ascent direction In particular, the steepest ascent step may then be preferred since it has, by definition, also an ascent direction but requires a much smaller computational effort and is steepest.

On the other hand, if the computationaleffort of the Fisher scoring step is no impediment, maximizingthe log-likelihood function using the Fisher scoring method only from start till convergence is much simpler than starting with the steepest ascent method followed by switching to the Fisher scoring method when appropriate Furthermore,near the maximum the full Fisher scoring step approximates the Newton step if the number of observations is not too small Checking the eigenvalues of the Hessian matrix of the log-likelihood function is not needed Therefore, in any case, the Fisher scoring method is much simpler and much less computationally demanding than the Newton method The computational effort involved in the Fisher scoring method is further reduced by the fact that computing the Fisher information matrix requiresfirst-orderderivatives of the log-likelihood function with respect to the parameters only as (438) shows EXAMPLE6.

ax that is, the direction opposed to that of the gradient, is, by definition, a direction in which the function decreases. The direction of any K x 1 vector y is called a descent direction if the scalar

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