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For the earlier three versions of the test, the prototypical riad of distractibility subtests (Digit Span, Arithmetic, Digit Symbol) had the highest loadings on each of the dimensions. However, on the WAIS-III, the three-factor solution had loadings from the traditional three distractibility tasks plus strong loadings from the new tasks on the Working Memory Index (Letter-Number Sequencing) and the Processing Speed Index (Symbol Search). The third factor on the WAIS-III, being a combination of Working Memory and Processing Speed tasks, seems to stress working memory in both the visual and verbal modalities (Kaufman et al., 2001). The three-factor solutions in the WAIS-R and the WAIS-III are quite reasonably congruent (.984) when comparing the third WAIS-R dimension with the third WAIS-III factor obtained from an analysis of only the 11 subtests shared with the WAIS-R (Kaufman et al., 2001). Draw datamatrix 2d barcode for vb.net using .net .Related: QR Code Generator .NET , Code 39 Generation .NET , .NET Code 128 Generation

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Additional Reading Material on Accuracy The trade-o between training error and generalization has prompted much research in the generalization performance of NNs Average generalization performance has been studied theoretically to better understand the behavior of NNs trained on a nite data set Research shows a dependence of generalization error on the training set, the network architecture and weight values Schwartz et al [767] show the importance of training set size for good generalization in the context of ensemble networks Other research uses the VC-dimension (Vapnik-Chervonenkis dimension) [8, 9, 152, 643] to derive boundaries on the generalization error as a function of network and training set size Best known are the limits derived by Baum and Haussler [54] and Haussler et al [353] While these limits are derived for, and therefore limited to, discrete input values, Hole derives generalization limits for real valued inputs [375] Limits on generalization have also been developed by studying the relationship between training error and generalization error Based on Akaike s nal prediction error and information criterion [15], Moody derived the generalized prediction error which gives a limit on the generalization error as a function of the training error, training set size, the number of e ective parameters, and the e ective noise variance [603, 604] Murata et al [616, 617, 618] derived a similar network information criterion Using a di erent approach, ie Vapnik s Bernoulli theorem, Depenau and M ller [202] derived a bound as a function of training error, the VC-dimension and training et size These research results give, sometimes overly pessimistic, limits that help to clarify the behavior of generalization and its relationship with architecture, training set size and training error Another important issue in the study of generalization is that of over tting Over tting means that the NN learns too much detail, e ectively memorizing training patterns This normally happens when the network complexity does not match the size of the training set, ie the number of adjustable weights (free parameters) is larger than the number of independent patterns If this is the case, the weights learn individual patterns and even capture noise This over tting phenomenon is the consequence of training on a nite data set, minimizing the empirical error function given in equation (72), which di ers from the true risk function given in equation (71) Amari et al developed a statistical theory of overtraining in the asymptotic case of large training set sizes [22, 21] They analytically determine the ratio in which patterns should be divided into training and test sets to obtain optimal generalization performance and to avoid over tting Over tting e ects under large, medium and small training set sizes have been investigated analytically by Amari et al [21] and M ller et al [612] u.

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