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Text normalization is the process of transforming text into a single canonical form that it might not have had before. Normalizing text before storing or processing it allows for separation of concerns, since input is guaranteed to be consistent before operations are performed on it. Text normalization requires being aware of what type of text ...
The default Excel 2007 and later workbook format. In reality, a ZIP compressed archive with a directory structure of XML text documents. Functions as the primary replacement for the former binary .xls format, although it does not support Excel macros for security reasons. Saving as .xlsx offers file size reduction over .xls [39] Excel Macro ...
In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some ...
Selecting the target range depends on the nature of the data. The general formula for a min-max of [0, 1] is given as: [3] ′ = () where is an original value, ′ is the normalized value. For example, suppose that we have the students' weight data, and the students' weights span [160 pounds, 200 pounds].
To quantile normalize two or more distributions to each other, without a reference distribution, sort as before, then set to the average (usually, arithmetic mean) of the distributions. So the highest value in all cases becomes the mean of the highest values, the second highest value becomes the mean of the second highest values, and so on.
Line breaks normalized to #xA on input, before parsing; Attribute values are normalized, as if by a validating processor; Character and parsed entity references are replaced; CDATA sections are replaced with their character content; The XML declaration and document type declaration are removed; Empty elements are converted to start-end tag pairs
This solves the problem of different features having vastly different scales, for example if one feature is measured in kilometers and another in nanometers. Activation normalization, on the other hand, is specific to deep learning , and includes methods that rescale the activation of hidden neurons inside neural networks .
Using code-word lengths obtained from the page-hit counts returned by Google from the web, we obtain a semantic distance using the NCD formula and viewing Google as a compressor useful for data mining, text comprehension, classification, and translation. The associated NCD, called the normalized Google distance (NGD) can be rewritten as
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