Supervised and unsupervised learning for classifying changes in optical time domain reflectometer traces

Keyphrases

• Unsupervised Learning 100%
• Supervised Learning 100%
• Optical Time Domain Reflectometer 100%
• Random Forest 33%
• Classification Accuracy 33%
• Network Infrastructure 33%
• Multilayer Perceptron 33%
• Supervised Learning Model 33%
• Convolutional Neural Network 33%
• Telecommunications 33%
• Superior Performance 33%
• Optical Fiber 33%
• Supervised Model 33%
• Gaussian Mixture Model 33%
• Network Operator 33%
• Clustering Model 33%
• Event Detection 33%
• Fiber Loss 33%
• Distributed Effects 33%
• In-service Measurement 33%
• Stimulated Raman Scattering 33%
• Raman Amplification 33%
• Adjusted Rand Index 33%
• Normalized mutual Information 33%
• Single Effect 33%
• Multiple Effects 33%
• Unsupervised Learning Model 33%

Computer Science

• Supervised Learning 100%
• Unsupervised Learning 100%
• Mutual Information 50%
• Random Decision Forest 50%
• Classification Accuracy 50%
• Network Infrastructure 50%
• Network Operator 50%
• Convolutional Neural Network 50%
• Event Detection 50%
• Telecommunication 50%
• Superior Performance 50%
• Multilayer Perceptron 50%
• Gaussian Mixture Model 50%
• Research Paper 50%
• Raman Spectra 50%
• Raman Amplification 50%
• Adjusted Rand Index 50%