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Illustrated TCP/IP by Matthew G. Naugle Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471196568 Pub Date: 11/01/98

Figure 15.3. Grasp taxonomy by Th. Iberall [15].

best option with the bank being the next best choice. Even though the hotel investment has a higher return, it is riskier so likely the least attractive option. Note that if the business has issued both preferred and common stock, the return on stockholders equity equation can be modified, with the numerator becoming net income less preferred dividends and the denominator becoming average common stockholders equity. To the common stockholders, preferred stock is a form of debt on which a fixed dividend rate must be paid. To the extent that borrowing from preferred stockholders enhances profits and the added profits exceed the fixed rate of dividends paid to preferred stockholders, the additional earnings accruing to the common stockholders will be improved.

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The window menu (or system menu, as it used to be called) is the little menu that appears in some windows when you tap the window icon in the upper left corner of the title bar. A window with a window menu is shown in Figure 4.5.

This works well until the value goes over 127; then it becomes negative. This is because of the decision to make all bytes signed in Java. Instead the following must be done:

(a) k = 400, Cluster Count: 4

myLoadVars = new LoadVars();<<Loc: added empty parens here --IMD>> myLoadVars.load("http://www.myserver.com/data.txt");

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forced to minimize the error at (1200,2) This causes the linear consequent hyperplanes to go through the point (1200,2) A promising strategy to circumvent this problem lies in the adaptation of only those parameters that correspond to signi cantly active rules, that is, rules that possess normalized membership function values Y ~act higher than a certain x threshold near 0 This guarantees that rules that represent areas not lying near a constant process state remain unchanged and, hence, are not disturbed In FLEXFIS, this can be accomplished by replacing Step 9 in Algorithm 1 with the following condition: x Step 9: If Yi ~ k 1 ! thresh, perform recursive weighted least squares for the consequent parameters of the ith rule by This is demonstrated in the right images in Figure 54, where a threshold of 01 was used.

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Figure 20.4. Dry-cured ham at the end of drying and before submission to the cellar. Ham is completely covered by a fat layer.

In fact, setting the threshold is a crucial point for a well-performing adaptation: If it is set too close to 0, the unlearning effect remains; if it is set too close to 1, the consequent parameters are hardly updated and may not be able to follow new comming trends in the data Another point of view of the unlearning effect is when considering classi cation problems, where the data distribution of the classes gets quite unbalanced during the incremental learning process, so that one or more classes become signi cantly more frequent than some others In this case, the danger is high that the less-frequent classes are simply overwhelmed and do not contribute to a cluster or a rule with majority any longer, causing a bad classi cation accuracy for these classes [50].

The diagram in Figure 6.26 shows the logic flow that was used for both the encoding and decoding of this technique.

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