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For a quadratic classifier, the correct solution is assumed to be quadratic in the measurements, so y will be decided based on + + In the special case where each observation consists of two measurements, this means that the surfaces separating the classes will be conic sections (i.e., either a line , a circle or ellipse , a parabola or a ...
For given low class number (such as 1, 2, and 3), Gauss gives lists of imaginary quadratic fields with the given class number and believes them to be complete. Infinitely many real quadratic fields with class number one Gauss conjectures that there are infinitely many real quadratic fields with class number one.
Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or ...
The first class is the discriminant of an algebraic number field, which, in some cases including quadratic fields, is the discriminant of a polynomial defining the field. Discriminants of the second class arise for problems depending on coefficients, when degenerate instances or singularities of the problem are characterized by the vanishing of ...
Qualitative Data Analysis as used in qualitative research; Quadratic discriminant analysis as used in statistical classification or as a quadratic classifier in machine learning; The .QDA filename extension, used for Quadruple D archives
Kernel discriminant analysis has been used in a variety of applications. These include: Face recognition [3] [8] [9] and detection [10] [11] Hand-written digit recognition [1] [12] Palmprint recognition [13] Classification of malignant and benign cluster microcalcifications [14] Seed classification [2] Search for the Higgs Boson at CERN [15]
Mahalanobis distance is widely used in cluster analysis and classification techniques. It is closely related to Hotelling's T-square distribution used for multivariate statistical testing and Fisher's linear discriminant analysis that is used for supervised classification .
The class of bilinear (or quadratic) time–frequency distributions can be most easily understood in terms of the ambiguity function, an explanation of which follows. Consider the well known power spectral density P x ( f ) {\displaystyle P_{x}(f)} and the signal auto-correlation function R x ( τ ) {\displaystyle R_{x}(\tau )} in the case of a ...