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The Gram matrix is symmetric in the case the inner product is real-valued; it is Hermitian in the general, complex case by definition of an inner product. The Gram matrix is positive semidefinite, and every positive semidefinite matrix is the Gramian matrix for some set of vectors. The fact that the Gramian matrix is positive-semidefinite can ...
The Harris–Benedict equation (also called the Harris-Benedict principle) is a method used to estimate an individual's basal metabolic rate (BMR).. The estimated BMR value may be multiplied by a number that corresponds to the individual's activity level; the resulting number is the approximate daily kilocalorie intake to maintain current body weight.
Glycemic load of a 100 g serving of food can be calculated as its carbohydrate content measured in grams (g), multiplied by the food's GI, and divided by 100. For example, watermelon has a GI of 72. A 100 g serving of watermelon has 5 g of available carbohydrates (it contains a lot of water), making the calculation (5 × 72)/100=3.6, so the GL ...
The Schofield Equation is a method of estimating the basal metabolic rate (BMR) of adult men and women published in 1985. [1] This is the equation used by the WHO in their technical report series. [2] The equation that is recommended to estimate BMR by the US Academy of Nutrition and Dietetics is the Mifflin-St. Jeor equation. [3]
Findings are inconclusive because alcohol itself contains seven calories per gram, but research suggests that the body only extracts 70-80 percent of this due to thermogenesis, thus the approximate number of calories that can be utilized is between 5 and 6 calories per gram of alcohol.
Based on the work of Atwater, it became common practice to calculate energy content of foods using 4 kcal/g for carbohydrates and proteins and 9 kcal/g for lipids. [2] The system was later improved by Annabel Merrill and Bernice Watt of the USDA, who derived a system whereby specific calorie conversion factors for different foods were proposed. [3]
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Low-rank matrix approximations are essential tools in the application of kernel methods to large-scale learning problems. [ 1 ] Kernel methods (for instance, support vector machines or Gaussian processes [ 2 ] ) project data points into a high-dimensional or infinite-dimensional feature space and find the optimal splitting hyperplane.