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TABLE 22.5 Size of Each Component of Authentication Message in SAS-UAB Components Size Issuer 8 Receiver 8 B 8 Enc(PMK) 40 SP 8 Exp 20 Sig 8

Thick, clayey Mud-rich sediment, low K (e.g. glacial till or lacustrine clays)

700 + 900 700 + 1100 900 + 1100 700 + 1300 900 + 1300 1100 + 1300 700 + 1500 900 + 1500 1100 + 1500 1300 + 1500

delivers the optimal solution in the least-squares sense. It can be seen that the model quality of the samplewise evolved model is not signi cantly worse than the quality of the model trained in batch mode. This means that the evolved solution comes close to the optimal one. Integrating all the concepts mentioned previously, the algorithm of FLEXFIS can be summarized as in Algorithm 1 (for further details about the derivation of this algorithm please refer to [41]).

(7.167)

q(x)

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// retrieve a channel s current sample description, the // channel returns a sample description that is // appropriate to the type of data being captured err = SGGetChannelSampleDescription(gSGChanVideo, (Handle)imageDesc); BailErr(err); // make a scaling matrix for the sequence sourceRect.right = (**imageDesc).width; sourceRect.bottom = (**imageDesc).height; RectMatrix(&scaleMatrix, &sourceRect, &gBoundsRect);

Range( D14:G20 ).Select ActiveWorkbook.Names.Add Name:= InputArea , _ RefersToR1C1:= =Sheet1!R14C4:R20C7

problem However, for a particular classi cation problem, these measures may not select the optimal model The reason is that each classi cation problem carries with it a unique set of costs and bene ts, which stem from the particular set of circumstances unique to that business or research problem The cost of a false positive (wrongly predicting positive response) may be low in certain environments but higher in other environments For example, in direct marketing, a false positive may cost no more than a postcard, while in HIV testing, a false positive on the ELISA test will be more expensive, leading to second-level HIV testing (On the other hand, of course, false negatives in HIV testing are very serious indeed, which is why the ELISA test allows a higher rate of false positives, to maintain the false negative rate as low as possible.

[T]he aggregate of the speci c educational, scienti c, and professional contributions of the discipline of psychology to the promotion and maintenance of health, the prevention and treatment of illness, the identi cation of etiologic and diagnostic correlates of health and illness and related dysfunctions, and the analysis and improvement of the health care system and health policy.

) In business problems, such as our direct mail marketing problem, company managers may require that model comparisons be made in terms of cost/bene t analysis Recall from Discovering Knowledge in Data: An Introduction to Data Mining [2] that it is useful to construct a cost/bene t table when performing classi cation This is done to provide model comparison in terms of anticipated pro t or loss by associating a cost or bene t with each of the four possible combinations of correct and incorrect classi cations Let us consider each of the four possible decision outcomes (true negative, true positive, false negative, and false positive) and assign reasonable costs to each decision outcome 1 True negative (TN) The model predicted that this customer would not respond to the direct mail marketing promotion, so no postcard was mailed to him or her.

In reality, this customer would not have responded to the promotion Therefore, the correct decision was made No costs were incurred, since no postcard was sent; no sales were made, and no prospective sales were lost 2 True positive (TP) The model predicted that this customer would respond to the direct mail marketing promotion, so a promotion was mailed to him or her In reality, this customer would indeed have responded to the promotion Therefore, again the correct decision was made The direct mailing cost, with materials, postage, and handling, is $2 per promotion unit mailed However, this particular TP customer, upon receiving the postcard, would have come into the store to make purchases The question then becomes: How much money would we reasonably expect the customer to spend, and how much of that amount spent could be considered pro t Table 7.

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