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In many applications where classified data are very expensive to acquire, using an independent test set may be a very wasteful procedure. A number of alternative procedures, all based on resampling schemes, have been investigated. These could be broadly described as attempts to use the full set of training data to produce corrected for the bias of the apparent error rate. Hand (1997) an estimate of identifies three variants of resampled error rate estimation:

8.31 7.58 7.30

1. the leave-one-out estimate;

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This affects only tracks for which the root note has been set, and will be discussed later in this chapter in the section on track/clip properties It is easy to rearrange the order in which the tracks appear Click track 1, either on the track name or the icon with the track number and type Now drag the track downward until the cursor is below the second track and let go Notice how the track order was rearranged The playback will still sound identical, however The reason you might want to rearrange tracks is primarily so that related tracks are visually close to each other You can also cut, copy, paste, and delete entire tracks Select a track by clicking on the track name or the icon with the track number and type Now press the Delete key You will see the entire track disappear.

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Barcode in Excel 2010 - Super User
The easiest way to do this is to use a font which is a barcode. I've done this and used the Code 39 from ... Active: 3 years, 9 months ago

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Make sure the TechnoRiver Barcode Font package and Barcode Font Excel Add- In are properly installed as described above. Launch Microsoft Excel . In your ...

1993 6.65 6.64 6.34 5.90 8.08 6.45 6.90 6.38 5.30 6.66 6.48 6.69 5.48 5.36 6.20 6.07 6.58 6.65 6.33 6.12 8.20 7.99 6.27 7.67 6.74 7.14 6.56 5.78 6.28 6.43 6.50 5.66 5.38 6.65 6.56 6.97 7.17 6.92 6.65 7.62 7.87 5.94 6.25 7.40 7.45 6.98 7.20 6.00 5.82 6.08 6.36 6.01 5.80 7.02 6.84 7.11 7.22 7.01 6.59 7.54 7.51 6.55 6.18 7.00 7.57 6.74 7.05 6.01 5.87 6.68

10-yr. av. 6.38 6.18 6.20 6.04 5.34 5.00 5.21 4.85 5.04 5.17 4.89 5.33 5.75 5.51 4.92 5.24 5.07 4.89 5.39 5.21 5.19 4.87 5.58 5.53 6.09 5.98 5.78 5.85 4.91 5.16 5.46 5.45 4.52 4.34 5.81 5.65 5.92 5.82 6.28 6.02 5.49 5.93 4.82 4.72 6.55

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where the notation p ( " ) indicates that p is calculated leaving out the n t hobservabion but then evaluated for the observation. It can be shown that this has a reduced bias compared to but there is evidence that this estimator has a relatively large variance compared to v-fold cross-validation (Ripley, 1996, 52.7). For v-fold cross-validation the sample is divided into parts (which we assume v-1 are equal in size) and the classifier is then trained on -of the sample and tested on the excluded l / v t h part. This is done, in turn, for each of the v parts and the average of the errors is taken. The bootstrap attempts to estimate the bias = The procedure is to sample, with replacement, from the training data, T. let T k be the k t h "bootstrap" sample and let be the apparent error rate on Let be the apparent error rate of the original data with the classifier developed on the T k bootstrap sample. Then the k t h bias estimate is B k = We then estimate the bias as = 1/K Bk, where we have samples from the training data. Then

Press Ctrl+Z to undo this action Now press Ctrl+X to cut the selection (the scissors icon in the ACID toolbar has the same effect) You will again see the entire track disappear, but this time the track is sitting in the computer s clipboard, waiting to be pasted Press Ctrl+V to paste the selection, and you will see that the track has returned Press Ctrl+V one more time just to prove that you are indeed pasting a track from the clipboard You will notice that there are now two identical tracks Press Ctrl+Z to revert your project to just two tracks Now select the other track and press Ctrl+C to copy it You won t see anything disappear, but ACID has made a copy of the track and put it into its clipboard.

7.07 5.64 5.76 5.01 5.11 4.88

When an observation from T is in T k then the classifier trained on T k may predict that observation well. In order to overcome this dependence on the presence or otherwise of particular observations, Efron (1983) suggested the "0.632" estimator as a variant on the bootstrap. Let P t T Gbe the apparent error rate of those observations in the original data that do appear in the k t h bootstrap sample. Then let = 1/K The "0.632" estimator is then

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