31.Knowledge and Data Engineering by John G. Webster (Editor) PDF

31.Knowledge and Data Engineering by John G. Webster (Editor) PDF

By John G. Webster (Editor)

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Forecasting levels of bad loans and fraudulent credit card usage, credit card spending patterns of new customers, and which kinds of customers will respond to (and qualify for) new loan offers. Manufacturing and Production. Predicting when to expect machinery failures; finding key factors that control optimization of manufacturing capacity; predicting excessive vibrations in a steel mill when rolling; determining values for circuit trim resistors. Astrophysics. Modeling known phenomena to allow automatic discovery in new data; distinguishing between stars and galaxies in faint images; discovering comets in terabytes of image data originally collected for other scientific purposes.

We see that Pwin of H1 increases toward one when the number of samples increases. ) In contrast, Pwin of H2 reduces to zero when the number of samples is increased. 0, which means H3 is definitely better than H0. Note that Pwin considers both the mean and variance. 5, it is not clear whether the hypothesis is better than or worse than the baseline. Given baseline hypothesis H0, we now show Pwin of Hi in subdomain j with respect to the average performance of H0. Assuming sample mean Ȑˆ i, j, sample variance ␴ˆ i,2 j, and ni, j test cases, Pwin is defined as follows:  Pwin (i, j) = Ft ni, j − 1, µ ˆ i, j σˆ i,2 j /ni, j   (8) where Ft(␯, x) is the cumulative distribution function of Student’s t-distribution with ␯ degrees of freedom, and Pwin(i, j) is the probability that the true performance (population mean) of Hi in subdomain j is better than that of H0.

The problem of learning the quantitative structure, or the CPTs, is represented in Table 3: learn each of the ␪s shown in these tables. Each ␪ represents an unknown conditional probability, one per cell. The ␪s within any particular column of a table are dependent: they must all add to 1. Learning the ␪s is typically treated as a form of statistical induction. Let ␪ represent the unknown probability of some event occurring. Statistical induction is the task of estimating ␪ from a sequence of observations of the event that it describes.

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31.Knowledge and Data Engineering by John G. Webster (Editor)

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