アルゴリズム:Algorithms Simulation, Data Science and Artificial Intelligence
Topics on simulation and data science and artificial intelligence used for digital transformation , artificial intelligence and machine learning tasks
アルゴリズム:Algorithms
アルゴリズム:Algorithms IOT技術:IOT Technology Protected: Differences between hidden Markov models and state-space models and parameter estimation for state-space models
Differences between state-space models, Bayesian models, and hidden Markov models used in digital transformation, artificial intelligence, and machine learning tasks, and parameter estimation for state-space models
Stream Data Processing Protected: Time Series Data Analysis (1) – State Space Model
Overview of various state-space models linear and Gaussian state-space models, AR models, autoregressive and moving average ARMA models, component decomposition models, and time-varying coefficient models) for time series data analysis used in digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms Protected: Structural Regularization Learning with Submodular Optimization (3)Formulation of the structural regularization problem with submodular optimization
Application of submodular function optimization, an optimization method for discrete information, to structural regularization problems and formulations using submodular optimization (linear approximation and steepest effect methods, accelerated proximity gradient method, FISTA, parametric submodular minimization, and splitting algorithms)
アルゴリズム:Algorithms Protected: Structural regularization learning using submodular optimization (2) Structural sparsity obtained from submodular functions
Structural regularization learning (coupled Lasso and Lovász extensions) by structural sparsity obtained from submodular functions in submodular optimization, an optimization method for discrete information used in digital transformation, artificial intelligence, and machine learning tasks.
IOT技術:IOT Technology Protected: Structural regularization learning with submodular optimization (1) Regularization and p-norm review
Review of sparse modeling, regularization and p-norm to consider structural regularization learning with submodular optimization, an optimization technique for discrete information for digital transformation, artificial intelligence and machine learning tasks
IOT技術:IOT Technology Protected: Maximum Flow and Graph Cut (4) Graphically Representable Submodular Functions
Maximum flow algorithms and pre-flow push methods in graphically representable submodular functions for submodular optimization, an optimization approach for discrete information utilized in digital transformation, artificial intelligence, and machine learning tasks
Symbolic Logic Protected: Maximum Flow and Graph Cut (3) Inference and Graph Cut in Markov Stochastic Fields
Inference and graph cuts in Markov stochastic fields for graph maximal flow extraction by undermodular optimization, a discrete information optimization method for digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms Protected: Maximum Flow and Graph Cutting (2)Maximum Flow Algorithm
Ford-Fulkerson's algorithm and Goldberg-Tarjan's algorithm for the maximum flow problem for directed graphs used in digital transformation, artificial intelligence, and machine learning tasks, pre-flow and push methods, increasing path algorithms, and residual networks
IOT技術:IOT Technology Protected: Maximization of submodular functions and application of the greedy method (1) Overview of the greedy method and its application to document summarization
Optimization methods for discrete information used in digital transformation, artificial intelligence, and machine learning tasks: application of greedy methods to undermodular function maximization and its use in document summarization tasks