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In geometry, the hinge theorem (sometimes called the open mouth theorem) states that if two sides of one triangle are congruent to two sides of another triangle, and the included angle of the first is larger than the included angle of the second, then the third side of the first triangle is longer than the third side of the second triangle. [1]
The hinge loss is a convex function, so many of the usual convex optimizers used in machine learning can work with it. It is not differentiable , but has a subgradient with respect to model parameters w of a linear SVM with score function y = w ⋅ x {\displaystyle y=\mathbf {w} \cdot \mathbf {x} } that is given by
Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization which may be considered a quasi-Newton method. SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable , but not necessarily convex.
In computer science and mathematical logic, satisfiability modulo theories (SMT) is the problem of determining whether a mathematical formula is satisfiable.It generalizes the Boolean satisfiability problem (SAT) to more complex formulas involving real numbers, integers, and/or various data structures such as lists, arrays, bit vectors, and strings.
The NFL playoff schedule is about to be set, with the wild-card dates and times for every matchup to be revealed during Week 18.
For example, if you weigh 150 pounds, that’s at least 52.5 grams of protein daily. But here’s the catch:, Building muscle requires eating significantly more protein than just maintaining the ...
Call. If you want to see a duck that looks like it's ready to be cuddled to sleep, then you'll want to check out the Call duck. This breed is known for its large and fluffy body, which includes a ...
Bennett's inequality, an upper bound on the probability that the sum of independent random variables deviates from its expected value by more than any specified amount Bhatia–Davis inequality , an upper bound on the variance of any bounded probability distribution