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Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning
Chien Chang Chen
, Hung Hui Juan, Meng Yuan Tsai, Henry Horng Shing Lu
認知智慧與精準健康照護研究中心
生醫科學與工程學系
研究成果
:
雜誌貢獻
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期刊論文
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同行評審
27
引文 斯高帕斯(Scopus)
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Keyphrases
Density Functional Theory
100%
Pattern Recognition
100%
Unsupervised Learning
100%
Machine Learning
100%
Unsupervised Pattern
100%
Biological Data
100%
Theory Learning
100%
Learning Recognition
100%
Number of Clusters
66%
Cluster Boundary
66%
Most Probable
66%
Biological Systems
33%
Physical Theory
33%
Clinical Diagnosis
33%
Machine Learning Techniques
33%
Clustering Results
33%
High-accurate
33%
Searching Process
33%
Magnetic Resonance Imaging
33%
Method Validation
33%
Detour
33%
Hohenberg-Kohn Theorem
33%
Density Function
33%
Brain Tumor Detection
33%
High-order Neural Networks
33%
System of Interest
33%
Brainbow
33%
Fisher's Iris Dataset
33%
Atomic Clusters
33%
Interdisciplinary Problem
33%
Computer Science
Data Structure
100%
Density Functional Theory
100%
Unsupervised Learning
100%
Pattern Recognition
100%
Machine Learning
100%
Learning System
100%
Experimental Result
33%
Biological System
33%
Clustering Result
33%
Searching Process
33%
Density Function
33%
Method Validation
33%
High Dimensional Neural Network
33%
Tumor Detection
33%
Mathematics
Data Structure
100%
Density Functional
100%
Pattern Recognition
100%
Neural Network
33%
Physical Theory
33%
Searching Process
33%
Clinical Diagnosis
33%
Plausibility
33%
Biological System
33%
Density Function
33%
Magnetic Resonance Imaging
33%
Material Science
Density
100%
Pattern Recognition
100%
Tumor
25%
Magnetic Resonance Imaging
25%
Chemical Engineering
Learning System
100%
Unsupervised Learning
100%
Neural Network
33%