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早稲田大学 ホーム
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Scopus著者プロファイル
村田 昇
教授
Professor 教授
,
先進理工学部
ウェブサイト
https://w-rdb.waseda.jp/html/100000581_ja.html
h-index
2854
被引用数
22
h 指数
Pureの文献数とScopusの被引用数に基づいて算出されます
1994 …
2025
年別の研究成果
概要
フィンガープリント
ネットワーク
研究成果
(145)
類似のプロファイル
(6)
フィンガープリント
Noboru Murataが活動している研究トピックを掘り下げます。このトピックラベルは、この研究者の研究成果に基づきます。これらがまとまってユニークなフィンガープリントを構成します。
並べ替え順
重み付け
アルファベット順
Engineering
Artificial Neural Network Model
16%
Autoencoder
37%
Box Model
83%
Circuit Design
38%
Conditional Entropy
25%
Consumption Pattern
33%
Continuous Casting Process
17%
Conventional Method
29%
Convolutional Neural Network
47%
Data Show
16%
Deep Learning
49%
Deep Neural Network
41%
Demand Profile
16%
Design Optimization
41%
Diffraction Limit
16%
Direct Connection
27%
Distorted Image
25%
Electric Machine
16%
Electricity Consumption
16%
Energy Systems
16%
Experimental Result
31%
Field Computation
16%
Finite Element Method
33%
Generalized Gaussian Distribution
16%
Independent Component Analysis
33%
Laplace Operator
22%
Level Set Method
38%
Localization Problem
33%
Loss Function
41%
Magnetic Circuit
38%
Magnetic Field
31%
Matrix Factorization
33%
Molten Steel
100%
Objective Function
40%
Observed Data
22%
Operational Planning
16%
Optimization Method
32%
Physical Quantity
16%
Probability Density Function
16%
Random Forest
24%
Real Data
30%
Regression Function
25%
Reluctance Motor
50%
Similarities
19%
Solar Energy
16%
Sparse Coding
33%
Statistical Model
43%
Steel Making
20%
Support Vector Machine
16%
Theoretic Quantity
16%
Mathematics
Apply It
16%
Arithmetic Operation
16%
Changepoint Detection
33%
Channel Capacity
16%
Clustering
16%
Conditional Entropy
22%
Continuous Distribution
16%
Convergence Property
25%
Deep Learning
18%
Density Function
16%
Em Algorithm
56%
Estimation Method
43%
Factorization
50%
Geometric Interpretation
16%
Geometrical Property
25%
Increasing Function
16%
Inferential Statistics
33%
Information Content
50%
Information Geometry
45%
Integral Transform
25%
Inverse Function
25%
Kernel Method
25%
Linear Combination
45%
Linear Regression
33%
Loss Function
16%
Manifold
35%
Matrix (Mathematics)
66%
Matrix Inversion
16%
Maximum Entropy
20%
Mixture Model
27%
Multivariate Analysis
22%
Naïve Bayes
33%
Neural Network
33%
Numerical Experiment
55%
Observed Data
25%
Parameter Estimation
26%
Parametric
33%
Principal Component Analysis
33%
Probability Distribution
16%
Probability Model
50%
Probability Theory
16%
Programming Problem
22%
Pythagorean
25%
Real-World Data
25%
Ridge Functions
33%
Ridgelets
16%
Simplex
25%
Statistical Independence
25%
Support Vector Machine
66%
Wavelet Analysis
16%
Keyphrases
Acoustic Signal
25%
Additive Mixing
16%
Artificial Neural Network Model
16%
Blind Source Separation
16%
Boosting Algorithm
22%
Bregman Divergence
25%
Calibrated Images
25%
Categorical Distribution
16%
Conditional Entropy
33%
Continuous Distribution
25%
Density Function
16%
Double Sparsity
20%
Entropy Minimization
33%
Estimation Method
16%
Flow Information
25%
Generalization Performance
22%
Grey-box Model
50%
Hessian
16%
Imaging Data
16%
Information Geometry
41%
KL Divergence
16%
Loss Function
45%
Minor Component Analysis
16%
Mixture Modeling
16%
Modal Linear Regression
33%
Molten Steel Temperature
50%
Multi-frame Super-resolution
25%
Multi-objective Topology Optimization
33%
Multiple Kernel Learning
33%
Naive Bayes Model
25%
Non-negative Matrix Factorization
33%
Nonparametric
25%
Online Algorithms
25%
Online Learning
16%
Parameter Calibration
25%
Photovoltaic Utilizations
33%
Plackett-Luce Model
16%
Preference Parameters
16%
Radially Symmetric
33%
Robust Loss Function
33%
Rotationally Symmetric
16%
Sequential Extraction
16%
Similarity Matrix
19%
Sparse Coding
33%
Statistical Model
31%
Symmetric Distortion
33%
Temps
20%
Transition Probability Matrix
16%
Tundish
50%
Weak Independence
16%
