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Bayesian Methods for Multiway Modeling and Online Hierarchical Clustering
Philip Johan Havemann Jørgensen
Cognitive Systems
Department of Applied Mathematics and Computer Science
Research output
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Book/Report
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Ph.D. thesis
203
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Dive into the research topics of 'Bayesian Methods for Multiway Modeling and Online Hierarchical Clustering'. Together they form a unique fingerprint.
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Keyphrases
Hierarchical Clustering
100%
Multi-way
100%
Machine Learning Algorithms
100%
Probabilistic Framework
100%
Bayesian Methods
100%
Bayesian Statistics
100%
Decomposition Method
50%
Chromatographic Data
50%
Probabilistic Method
50%
Model Complexity
50%
Online Algorithms
50%
Expert Knowledge
50%
Machine Learning Techniques
50%
Probabilistic Approach
50%
Bayesian Clustering
50%
Bayesian Inference
50%
One-class Classification
50%
Stream-based
50%
Unsupervised Method
50%
PARAFAC2
50%
Probabilistic Machine Learning
50%
Unsupervised Machine Learning Algorithms
50%
Binary-class Classification
50%
Food Authentication
50%
Computer Science
Machine Learning Algorithm
100%
Hierarchical Clustering
100%
Probabilistic Framework
66%
Machine Learning
33%
Authentication
33%
Analysed Data
33%
Model Complexity
33%
Data Stream
33%
Unsupervised Manner
33%
Class Classification
33%
Decomposition Method
33%
on-line algorithm
33%
Expert Knowledge
33%
Probabilistic Approach
33%
Probabilistic Method
33%
Engineering
Machine Learning Algorithm
100%
Hierarchical Clustering
100%
Probabilistic Framework
66%
Ground Truth
66%
Real Data
33%
Probabilistic Method
33%
Probabilistic Approach
33%
Data Stream
33%
Machine Learning Method
33%
Tasks
33%