Uncategorized Linear and nonlinear models and their use
Linear Models and Nonlinear Models
Linear models and nonlinear models are distinguished based on the differen...
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Uncategorized Uncategorized Differences between BT and if-then systems and their applications
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Differences Between BT (Behavior Tree) and If-Then Systems
A Behavior Tre...
Uncategorized Roughness, Harmony and Japanese Culture
Amaterasu-Omikami and Susanoo-no-Mikoto
Takachiho in Miyazaki Prefecture is mentioned in the Kojiki (R...
Uncategorized Recommendation systems in Netflix
Netflix recommendation system
The Netflix recommendation system will be based on information such as a user'...
Uncategorized FIDO and Passkey
FIDO
FIDO (Fast Identity Online) is a standardisation framework for more secure and easier online authenticati...
Uncategorized Overview of ChebNet and Examples of Algorithms and Implementations
Overview of ChebNet
ChebNet (Chebyshev network) is a type of graph neural network (GNN) proposed by Deffer...
Uncategorized Protected: Explainable Machine Learning (18)Adversarial Examples
Explainable Machine Learning with Adversarial Sample Approach utilized for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Cyber Security, Surrogate Models, Neural Networks, Black Box Attack, Expectation Over Transformation algorithm, EOT, InceptionV3, TensorFlow, Fast gradient method, VGG16 classifier, ImageNet, adversarial patch, 1-pixel attack, L-BFGS method, Fast gradient sign method
Uncategorized Protected: Explainable Artificial Intelligence (15) model-independent interpretation (sharpley value)
Model-independent interpretation with Sharpe Ray values as explainable artificial intelligence used for digital transformation, artificial intelligence, and machine learning tasks breakDown, fastshap, R language, symmetry axioms, LIME, SHAP, sparse explanation, efficiency, symmetry, dummy, additivity principle, Sharpe Ray values, cooperative game theory
Uncategorized Protected: On-line Stochastic Optimization and Stochastic Dual Averaging (SDA) for Machine Learning
On-line stochastic optimization and stochastic dual averaging methods for machine learning (mirror image descent, strongly convex functions, convex functions, convergence rates, polynomial decay averaging, strongly convex regularization) for digital transformation, artificial intelligence and machine learning tasks.
Uncategorized Evaluation of clustering for familiarization with k-means
On the evaluation of clustering around k-means for digital transformation, artificial intelligence, and machine learning tasks curse of dimensionality, Mahalanobis distance, Davies-Bouldin index, Dunn index, squared error, RSME, cluster number estimation, inter-cluster density, intra-cluster density