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  2. Dimensional analysis - Wikipedia

    en.wikipedia.org/wiki/Dimensional_analysis

    In engineering and science, dimensional analysis is the analysis of the relationships between different physical quantities by identifying their base quantities (such as length, mass, time, and electric current) and units of measurement (such as metres and grams) and tracking these dimensions as calculations or comparisons are performed.

  3. Data augmentation - Wikipedia

    en.wikipedia.org/wiki/Data_augmentation

    Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.

  4. Curse of dimensionality - Wikipedia

    en.wikipedia.org/wiki/Curse_of_dimensionality

    There is an exponential increase in volume associated with adding extra dimensions to a mathematical space.For example, 10 2 = 100 evenly spaced sample points suffice to sample a unit interval (try to visualize a "1-dimensional" cube) with no more than 10 −2 = 0.01 distance between points; an equivalent sampling of a 10-dimensional unit hypercube with a lattice that has a spacing of 10 −2 ...

  5. Reference dimension - Wikipedia

    en.wikipedia.org/wiki/Reference_Dimension

    A reference dimension is a dimension on an engineering drawing provided for information only. [1] Reference dimensions are provided for a variety of reasons and are often an accumulation of other dimensions that are defined elsewhere [2] (e.g. on the drawing or other related documentation).

  6. Dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Dimensionality_reduction

    Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.

  7. Vector (mathematics and physics) - Wikipedia

    en.wikipedia.org/wiki/Vector_(mathematics_and...

    In the natural sciences, a vector quantity (also known as a vector physical quantity, physical vector, or simply vector) is a vector-valued physical quantity. [9] [10] It is typically formulated as the product of a unit of measurement and a vector numerical value (), often a Euclidean vector with magnitude and direction.

  8. Data model (GIS) - Wikipedia

    en.wikipedia.org/wiki/Data_model_(GIS)

    A hybrid topological data model has the option of storing topological relationship information as a separate layer built on top of a spaghetti data set. An example is the network dataset within the Esri geodatabase. [23] Vector data are commonly used to represent conceptual objects (e.g., trees, buildings, counties), but they can also represent ...

  9. Category:Data modeling - Wikipedia

    en.wikipedia.org/wiki/Category:Data_modeling

    In information system design, data modeling is the analysis and design of the information in the system, concentrating on the logical entities and the logical dependencies between these entities Contents

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