音声信号認識技術

アルゴリズム:Algorithms

Protected: Partitioning Method on Support Vector Machines (1) SMO Algorithm

Efficiency using the partitioning method (SMO algorithm) on support vector machines utilized for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks
Symbolic Logic

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
アルゴリズム:Algorithms

Protected: kernel function

General kernel functions in support vector machines used in digital transformation (DX), artificial intelligence (AI), and machine learning, and kernel functions on probabilistic, string, and graph-type data
アルゴリズム:Algorithms

Protected: Regression Analysis with Support Vector Machines (2) Approach to Nonlinear Regression Problems

Approaches to nonlinear regression problems with support vector machines (quantile regression, kernel quantile regression, heterogeneous distribution models, ε-insensitive loss functions) utilized in digital transformation, artificial intelligence, and machine learning tasks.
地理空間情報処理

Machine Learning Professional Series – Relational Data Learning Post-Reading Notes

Overview of relational data learning to extract the meaning and knowledge behind information used in digital transformation , artificial intelligence , and machine learning tasks.
python

Protected: Evolving Deep Learning with PyTorch(OpenPose, SSD, AnoGAN, Efficient GAN, DCGAN, Self-Attention GAN, BERT, Transformer, GAN, PSPNet, 3DCNN, ECO)

OpenPose, SSD, AnoGAN, Efficient GAN, DCGAN, Self-Attention GAN, BERT, Transformer, GAN using pytorch for Digital Transformation and Artificial Intelligence tasks, PSPNet,3DCNN,ECO and other advanced DNNs
機械学習:Machine Learning

Protected: Applying Deep Learning to Speech Recognition

Overview of neural network applications (TDNN, RNN, CNN) and deep learning applications (LSTM, CTC) for speech recognition technology used in digital transformation and artificial intelligence tasks
機械学習:Machine Learning

Protected: Speaker adaptation and speaker recognition

Speaker adaptation (HMM) to improve recognition accuracy for digital transformation and artificial intelligence tasks, and speaker recognition (maximum likelihood linear regression, MLLR, i-vector) for security and other applications.
機械学習:Machine Learning

Protected:

Overview of noise reduction techniques to improve speech recognition for digital transformation and artificial intelligence tasks (additive noise, multiplicative noise and active noise control, parallel model coupling, PMC, factorial HMM)
機械学習:Machine Learning

Protected: Large-vocabulary continuous speech recognition

Overview of large-vocabulary continuous speech recognition, a technology for recognizing unconstrained general speech in speech recognition technology used for digital transformation and artificial intelligence tasks.
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