Only thing I can think of is the shorhand for "discriminant" in maths, which is the value allowing to calculate the squareroots of a second degree equation.@
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To establish the theorem in full generality requires a certain amount of algebraic number theory machinery, such as the theory of valuations on number fields, or of relative discriminants between such number fields. However, the basic ideas can be presented without much of this machinery by ...
–4ac is called the discriminant and is denoted by d. the sign of plus (+) and minus (-) in the quadratic formula represents that there are two solutions for quadratic equations and are called the roots of the quadratic equation. root 1: \(\begin{array}{l}x_{1}=\frac{-b+\sqrt{...
where is the conductor of (which, in the case of number fields, is the absolute value of the discriminant of ) and is the degree of the extension of over . As before, we have the amplification phenomenon that the above near-optimal square root cancellation bound is implied by the weaker...
So the equations of motion for the ball from time 0 up to the time the first bounce begins are: The equation now comes out as the quadratic equation The discriminant of this equation is Since and and , we have , this means that and . If , this means that and . Whatever the case,...
Discriminant Analysis 7. What are the Differences Between Linear and Logistic Regression? Linear regression is used to predict the value of a continuous dependent variable with the help of independent variables. Logistic Regression is used to predict the categorical dependent variable with the help of...
LDA(Linear discriminant analysis not latent Dirichlet allocation):require normal, not good for few categories variables, compute the addition of Multivariate distribution, compute CI, suffer multicollinearitySVM: no distribution requirement, compute hinge loss, flexible selection of kernels for nonlinear ...
(e.g. RBF). The truth is that a Logistic Regression can also be used with a different kernel, but at that point you might be better off going for SVMs for practical reasons. Another related reason to use SVMs is if you are in a highly dimensional space. For example, SVMs have been...