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Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines , logistic regression , and artificial neural networks ).
Database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model. Normalization entails organizing the columns ...
For example, sulfuric acid (H 2 SO 4) is a diprotic acid. Since only 0.5 mol of H 2 SO 4 are needed to neutralize 1 mol of OH −, the equivalence factor is: feq (H 2 SO 4) = 0.5. If the concentration of a sulfuric acid solution is c (H 2 SO 4) = 1 mol/L, then its normality is 2 N. It can also be called a "2 normal" solution.
List of datasets in computer vision and image processing. Outline of machine learning. v. t. e. These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning ...
In mathematics, statistics, finance, [1] and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the answer of a problem to a simpler one. It is often used in solving ill-posed problems or to prevent overfitting. [2]
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1][2][3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one. The direct modeling of likelihood provides ...
Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
Standard solution. In analytical chemistry, a standard solution (titrant or titrator) is a solution containing an accurately known concentration. Standard solutions are generally prepared by dissolving a solute of known mass into a solvent to a precise volume, or by diluting a solution of known concentration with more solvent. [1]