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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2 February, 2021 in 246B - complex analysis, math.CV, math.NT | Tags: complex tori, elliptic curves, j-invariant, modular discriminant, modular forms, modular functions, Weierstrass elliptic function | by Terence Tao | 55 comments Previous set of notes: Notes 2. Next set of notes: Notes...
If are real and the discriminant is initially positive, we see that we start with two real zeroes centred around , which then approach each other until time , at which point the roots collide and then move off from each other in an imaginary direction. In the general case, we can obtain...
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–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{...
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...
The discriminant of this equation is Since and and , we have , this means that and . If , this means that and . Whatever the case, we have that is non-negative and is non-positive. So we can interpret the two roots as endpoints of a “0th bounce” which is already in progress ...
Calculating the discriminant: =16±√256−2522=16±√42=16±22 This gives us two possible values for a: a=182=9ora=142=7 Thus, we have: 1. If a=9, then b=16−9=72. If a=7, then b=16−7=9 In either case, we can express x as: x=√9+√7=3+√7 Thus, the squa...
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 ...