A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning

Earth and Planetary Sciences

• Algorithms 100%
• Centroid 14%
• Classification 28%
• Classifier 42%
• Comparison 100%
• Discriminant Analysis 14%
• Evaluation 14%
• Experiment 28%
• Forest 14%
• Gene Expression 14%
• Learning 100%
• Logistics 28%
• Machine Learning 100%
• Model 57%
• Norm 14%
• Normalisation 28%
• Regression 28%
• Rest 14%
• Show 28%
• Support Vector Machine 14%
• Vector 57%

Computer Science

• Accuracy 14%
• Algorithms 71%
• Classification Accuracy 14%
• Classifier 42%
• Cosine Similarity 28%
• Discriminant Analysis 14%
• Domains 100%
• Evaluation 14%
• Extracted Feature 14%
• Feature Vector 57%
• Few-Shot Learning 100%
• Linear Discriminant Analysis 28%
• Logistic Regression 28%
• Machine Learning 100%
• Machine Learning Algorithm 28%
• Models 57%
• Nave Bayes 14%
• Neighbor Classification 14%
• Normalization 14%
• Normalization Method 14%
• Random Decision Forest 14%
• Support Vector Machine 14%

Biochemistry, Genetics and Molecular Biology

• Accuracy 28%
• Classification 28%
• Gene Expression 14%
• Learning 100%
• Random Forest 14%
• Rest 14%
• Support Vector Machine 14%