A Semisupervised Classification Approach for Multidomain Networks with Domain Selection

Keyphrases

• Classification Approach 100%
• Semi-supervised Classification 100%
• Domain Selection 100%
• Multi-domain Networks 100%
• Target Domain 50%
• Performance Prediction 50%
• Network Classification 50%
• Domain-relevant 50%
• Prediction Accuracy 25%
• Machine Learning 25%
• Classification Accuracy 25%
• Network Applications 25%
• Label Propagation 25%
• Learning Methods 25%
• Classification Performance 25%
• Automatically Identify 25%
• Real-world Problems 25%
• Analytical Solution 25%
• Network Partitioning 25%
• Computational Procedures 25%
• Multiple Instances 25%
• Classification Results 25%
• Cross-domain 25%
• Sparse Weight 25%
• Selection Ability 25%
• Rapid Growth 25%
• Information Integration 25%
• Networked Learning 25%
• Network Prediction 25%
• Multi-domain Classification 25%
• Data Representation 25%
• Optimal Networks 25%
• Highest Weight 25%
• Integrated Learning 25%
• Data Integration 25%

Computer Science

• Prediction Performance 100%
• classification approach 100%
• Experimental Result 50%
• Classification Accuracy 50%
• Machine Learning 50%
• Learning System 50%
• Classification Performance 50%
• Real-World Problem 50%
• Data Source 50%
• Network Partition 50%
• Learning Network 50%
• Data Integration 50%
• Multiple Instance 50%
• classification result 50%
• Domain Information 50%
• Data Representation 50%
• Optimal Network 50%
• Selection Ability 50%
• Weight Property 50%