Sparsity

Protected: Quasi-Newton Methods as Sequential Optimization in Machine Learning (2)Quasi-Newton Methods with Memory Restriction

Quasi-Newton method with memory restriction (sparse clique factorization, sparse clique factorization, chordal graph, sparsity, secant condition, sparse Hessian matrix, DFP formula, BFGS formula, KL divergence, quasi-Newton method, maximal clique, positive definite matrix, positive definite matrix completion, positive define matrix composition, graph triangulation, complete subgraph, clique, Hessian matrix, triple diagonal matrix Hestenes-Stiefel method, L-BFGS method)

2023.03.07

アルゴリズム:Algorithmsスパースモデリング微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Theory of Noisy L1-Norm Minimization as Machine Learning Based on Sparsity (2)

Theory of noisy L1 norm minimization as machine learning based on sparsity for digital transformation, artificial intelligence, and machine learning tasks numerical examples, heat maps, artificial data, restricted strongly convex, restricted isometric, k-sparse vector, norm independence, subdifferentiation, convex function, regression coefficient vector, orthogonal complementary space

2023.02.06

アルゴリズム:Algorithmsスパースモデリング幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

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

2023.01.06

スパースモデリング機械学習:Machine Learning

Dreams, Brain and Machine Learning From Dream Theory to Dream Data Science

Confirmation of dream experience in sleep (REM and non-REM sleep) using dream theory (Freud, Hobson activation-synthesis hypothesis, Ripley, etc.), brain networks and fMRI and machine learning (support Vectonema machine, Bayesian linear model with sparsity introduced)

2022.08.04

アルゴリズム:Algorithms哲学:philosophy心理学:psychology機械学習:Machine Learning深層学習:Deep Learning

Protected: Machine Learning Based on Sparsity Overview

An overview of efficient machine learning using sparsity, L1 norm regularization

2021.05.09

機械学習:Machine Learning