Modified Newton Method

アルゴリズム:Algorithms

Protected: Confidence Region Methods in Sequential Optimization in Machine Learning

Confidence region methods (dogleg method, norm constraint, model function optimization, approximate solution of subproblems, modified Newton method, search direction, globally optimal solution, Newton method, steepest descent method, confidence region radius, confidence region, descent direction, step width) in continuous optimization in machine learning used for digital transformation, artificial intelligence, machine learning tasks.
アルゴリズム:Algorithms

Protected: Statistical Mathematical Theory for Boosting

Statistical and mathematical theory boosting generalized linear model, modified Newton method, log likelihood, weighted least squares method, boosting, coordinate descent method, iteratively weighted least squares method, iteratively reweighted least squares method, IRLS method, weighted empirical discriminant error, parameter update law, Hessian matrix, corrected Newton method, Newton method, Newton method, iteratively reweighted least squares method, IRLS method) used for digital transformation, artificial intelligence, machine learning tasks. iteratively reweighted least square method, IRLS method, weighted empirical discriminant error, parameter update law, Hessian matrix, corrected Newton method, modified Newton method, Newton method, Newton method, link function, logistic loss, logistic loss, boosting algorithm, logit boost, exponential loss, convex margin loss, adaboost, weak hypothesis, empirical margin loss, nonlinear optimization
アルゴリズム:Algorithms

Protected: Newtonian and Modified Newtonian Methods as Sequential Optimization in Machine Learning

Newton and modified Newton methods (Cholesky decomposition, positive definite matrix, Hesse matrix, Newtonian direction, search direction, Taylor expansion) as continuous machine learning optimization for digital transformation, artificial intelligence and machine learning tasks
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