distinguishing barcode scanners from the keyboard in winforms

distinguishing barcode scanners from the keyboard in winforms

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Metzger, D S, 223, 243 Metzler, C W, 223, 243, 479, 480, 484 Metzner, H L, 82, 91 Meuleman, C, 69 Meulenbeld, G J, 613 Meulenbelt, I, 186 Meyer, J M, 159, 164 Meyer, P S, 561 Meyer, R G, 254, 263 Meyer, T J, 278, 290 Meyerowitz, B E, 273, 288 Meyers, B, 503 Meyers, G S, 344, 363 Mezzaroma, I, 73 Miaskiewicz, S, 217 Michalek, A M, 550, 564 Michalek, J E, 189 Michaud, L, 455, 462 Michel, P, 250, 265 Michel, V, 612 Michel, Y, 92 Michell, M, 69 Michelson, D, 186 Michenbaum, D, 403, 411 Michie, S, 18, 19, 21, 22 Michielutte, R, 551, 565 Michultka, D M, 248, 265 Micozzi, M S.

b(xk | x1 , . . . , xk 1 )

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Both bivariate and multiple correlations can be collinear or singular. A bivariate collinear relationship has a correlation of .90 or above and is resolved by deletion of one of the two variables. With a multivariate collinear relationship, diagnosis is more dif cult because the collinearity is not necessarily apparent through examination of bivariate correlations. Instead, multivariate statistics are needed, such as squared multiple correlations (SMCs, in which each variable, in turn, serves as the DV with the others as IVs), tolerances (1 SMC), or collinearity diagnostics. SMCs are available through factor analysis and regression programs in statistical software packages. However, SMCs are not evaluated separately for each group if you are analyzing grouped data. PRELIS provides SMCs for structural equation modeling. Most modern programs automatically protect against singularity. Screening for collinearity that causes statistical instability is also routine with most programs because they have tolerance criteria for inclusion of variables. If the tolerance is too low, the variable does not enter the analysis. Default tolerance levels range between .01 and .0001, so SMCs are .99 to .9999 before variables are excluded. You may wish to take control of this process, however, by adjusting the tolerance level (an option with many programs) or deciding yourself which variables to delete instead of letting the program make the decision on purely statistical grounds. SAS, SYSTAT, and SPSS have recently incorporated collinearity diagnostics proposed by Belsley, Kuh, and Welsch (1980) in which both a condition index and variance proportions associated with each standardized variable are produced for each root (dimension, factor, principal component). Variables with large variance proportions are those with problems. Condition index is a measure of tightness or dependency of one variable on the others. The condition index is monotonic

, 592, 593, 594, 595, 596, 611 Miczek, K A, 152, 166 Middlestadt, S E, 224, 241 Miell, J, 385 Migliori, G B, 119 Mihailescu, S, 154, 166 Mikat, E M, 195, 214 Milani, R V, 349, 361 Milby, J B, 333, 336 Milgram, N, 44, 48 Milgrom, H, 104, 109, 115 Millar, J D, 575, 577, 587 Miller, A B, 80, 84, 89, 270, 291 Miller, A H, 80, 95 Miller, A J, 402, 411 Miller, A M, 528, 541 Miller, B, 107, 118 Miller, B A, 516, 528, 541 Miller, B C, 442, 459 Miller, C M, 261, 265 Miller, D G, 283, 289 Miller, D J, 101, 118 Miller, D R, 507 Miller, E, 39, 40, 48, 358 Miller, G E.

, 63, 72, 84, 89, 93, 380, 388 Miller, K W, 546, 567 Miller, L H, 523, 539 Miller, L S, 48 Miller, L V, 193, 214 Miller, N E, 5, 22, 76, 91 Miller, P M, 397, 410 Miller, S, 52, 53, 72, 277, 290, 337, 517, 538 Miller, T Q, 156, 167, 345, 362, 550, 553, 564 Miller, T R, 447, 462 Miller, W R, 109, 118 Milley, J E, 461 Milliken, J C, 361 Milliken, N, 536, 539.

You can use the Appearance panel in System Preferences to reverse this behavior, so that a plain click in the scroller moves the thumb to that location, and Option-click scrolls the document by one complete screen. Look for the setting labeled Click In The Scroll Bar To.

Group 2, which uses the idea of the growing learning algorithm [8 10] with variable (adjustable) number of neurons in order to t a prespeci ed granulation quality. Group 3, in which the concept of on-line unsupervised learning is used. Here it is assumed that all data are not available at once but rather come one by one in a nite (or even endless) sequence of data points. Then all neurons from the set of assumed core neurons are gradually adjusting their locations in the space, according to the newly arriving data. Our recent research results on this type of online learning algorithm can be found in [10, 11]. Among the most popular standard of ine learning algorithms from Group 1 are the self-organizing (Kohonen) maps [4] and the neural gas learning algorithm [5]. One modi cation of the of ine neural gas learning algorithm is presented in Section 11.3.

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