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Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing , it is also known as data normalization and is generally performed during the data preprocessing step.
Scalability, a computer or network's ability to function as the amount of data or number of users increases; Scaling along the Z axis, a technique used in computer graphics for a pseudo-3D effect; Reduced scales of semiconductor device fabrication processes (the ability of a technology to scale to a smaller process)
Comparison of the various grading methods in a normal distribution, including: standard deviations, cumulative percentages, percentile equivalents, z-scores, T-scores. In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured.
Simple Features (officially Simple Feature Access) is a set of standards that specify a common storage and access model of geographic features made of mostly two-dimensional geometries (point, line, polygon, multi-point, multi-line, etc.) used by geographic databases and geographic information systems.
This is common on standardized tests. See also quantile normalization. Normalization by adding and/or multiplying by constants so values fall between 0 and 1. This is used for probability density functions, with applications in fields such as quantum mechanics in assigning probabilities to | ψ | 2.
Features from accelerated segment test (FAST) is a corner detection method, which could be used to extract feature points and later used to track and map objects in many computer vision tasks. The FAST corner detector was originally developed by Edward Rosten and Tom Drummond, and was published in 2006. [ 1 ]
Instead of maintaining a dictionary, a feature vectorizer that uses the hashing trick can build a vector of a pre-defined length by applying a hash function h to the features (e.g., words), then using the hash values directly as feature indices and updating the resulting vector at those indices. Here, we assume that feature actually means ...
These features share similar properties with neurons in the primary visual cortex that encode basic forms, color, and movement for object detection in primate vision. [13] Key locations are defined as maxima and minima of the result of difference of Gaussians function applied in scale space to a series of smoothed and resampled images. Low ...