Markov's Inequality

アルゴリズム:Algorithms

Protected: Overview of C-Support Vector Machines by Statistical Mathematics Theory

Support vector machines based on statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks C-support vector machines (support vector ratio, Markov's inequality, probability inequality, prediction discriminant error, one-out-of-two cross checking method, LOOCV, the discriminant, complementarity condition, main problem, dual problem, optimal solution, first order convex optimization problem, discriminant boundary, discriminant function, Lagrangian function, limit condition, Slater constraint assumption, minimax theorem, Gram matrix, hinge loss, margin loss, convex function, Bayes error, regularization parameter)
スパースモデリング

Protected: Theory of Noisy L1-Norm Minimization as Machine Learning Based on Sparsity (1)

Theory of L1 norm minimization with noise as sparsity-based machine learning for digital transformation, artificial intelligence, and machine learning tasks Markov's inequality, Heffding's inequality, Berstein's inequality, chi-square distribution, hem probability, union Bound, Boolean inequality, L∞ norm, multidimensional Gaussian spectrum, norm compatibility, normal distribution, sparse vector, dual norm, Cauchy-Schwartz inequality, Helder inequality, regression coefficient vector, threshold, k-sparse, regularization parameter, inferior Gaussian noise
Exit mobile version
タイトルとURLをコピーしました