Support Vector Machine

Protected: Sparse learning based on group L1 norm regularization

Sparse machine learning based on group L1-norm regularization for digital transformation, artificial intelligence, and machine learning tasks relative dual gap, dual problem, gradient descent, extended Lagrangian function, dual extended Lagrangian method, Hessian, L1-norm regularization, and group L1-norm regularization, dual norm, empirical error minimization problem, prox operator, Nesterov's acceleration method, proximity gradient method, iterative weighted reduction method, variational representation, nonzero group number, kernel weighted regularization term, concave conjugate, regenerative kernel Hilbert space, support vector machine, kernel weight Multi-kernel learning, basis kernel functions, EEG signals, MEG signals, voxels, electric dipoles, neurons, multi-task learning

2023.04.06

アルゴリズム:Algorithmsグラフ理論スパースモデリング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: 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)

2023.02.09

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: Support Vector Machines for Weak Label Learning (2) Multi-Instance Learning

Extension of support vector machines utilized for digital transformation, artificial intelligence, and machine learning tasks; multi-instance learning approach with SVMs for weak-label learning problems (mi-SVM, MI-SVM)

2022.09.02

アルゴリズム:Algorithmsグラフ理論微分積分:Calculus最適化:Optimization機械学習:Machine Learning画像認識技術異常検知・変化検知確率・統計:Probability and Statistics線形代数:Linear Algebra自然言語処理:Natural Language Processing音声信号認識技術

Protected: Structured Support Vector Machines

SVM structure learning and parsing using the deletion plane method algorithm on support vector machines utilized for digital transformation, artificial intelligence, and machine learning tasks, and protein similarity sequence search

2022.08.08

Rアルゴリズム:Algorithms幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning画像認識技術異常検知・変化検知確率・統計:Probability and Statistics線形代数:Linear Algebra自然言語処理:Natural Language Processing音声信号認識技術

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: Support vector machine software and implementation

Classification and regression with SVM using R kernlab in support vector machines used for digital transformation, artificial intelligence and machine learning tasks and LIBSVM algorithms SMO algorithm, shrinking

2022.07.29

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning画像認識技術異常検知・変化検知確率・統計:Probability and Statistics線形代数:Linear Algebra自然言語処理:Natural Language Processing関係データ学習音声信号認識技術

Protected: Model selection and regularization path tracking (1) Cross-validation method

Cross-validation methods (k-partition cross-validation and one-out cross-validation) for selecting hyper-parameters such as regularization parameters for support vector machines utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.07.08

アルゴリズム:Algorithms数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Partitioning Methods in Support Vector Machines (2) DCDM Algorithm for Linear SVM

DCDM algorithm (dual coordinate descent method algorithm), an efficient algorithm for processing large amounts of (sparse) data on support vector machines (algorithm for linear SVM used in LIBLINEAR) used in digital transformation (DX), artificial intelligence (AI) and machine learning (ML) tasks.

2022.07.01

アルゴリズム:Algorithmsグラフ理論微分積分:Calculus時系列データ解析最適化:Optimization機械学習:Machine Learning画像認識技術異常検知・変化検知確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: Introduction to Optimization with Support Vector Machines: Optimality Conditions and Generic Solution Methods

Optimality conditions (strong duality and KKT) and generic solution methods (active set and interior point method) in support vector machines used for digital transformation, artificial intelligence and machine learning tasks

2022.06.17

Symbolic Logicアルゴリズム:Algorithmsスパースモデリング最適化:Optimization機械学習:Machine Learning画像認識技術確率・統計:Probability and Statistics自然言語処理:Natural Language Processing音声信号認識技術

Protected: Application of Support Vector Machines to Multi-Class Classification

Extension to multi-class classification of support vector machines utilized in digital transformation, artificial intelligence , and machine learning tasks (one-to-other, one-to-one, multi-class classification with two-class classification combinations using error correcting output codes and direct multi-class classification)

2022.05.19

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