Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Understanding the Yarowsky Algorithm
Computational Linguistics
Cross-media manifold learning for image retrieval & annotation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Learning context-sensitive similarity by shortest path propagation
Pattern Recognition
Semi-supervised ranking on very large graphs with rich metadata
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A nonparametric classification method based on K-associated graphs
Information Sciences: an International Journal
Multi-view laplacian support vector machines
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Semi-supervised protein function prediction via sequential linear neighborhood propagation
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Bidirectional-isomorphic manifold learning at image semantic understanding & representation
Multimedia Tools and Applications
A purity measure based transductive learning algorithm
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Low-rank coding with b-matching constraint for semi-supervised classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Feature enrichment and selection for transductive classification on networked data
Pattern Recognition Letters
| Hi-index | 0.15 |
Graph-based learning provides a useful approach for modeling data in classification problems. In this modeling scenario, the relationship between labeled and unlabeled data impacts the construction and performance of classifiers, and therefore a semi-supervised learning framework is adopted. We propose a graph classifier based on kernel smoothing. A regularization framework is also introduced, and it is shown that the proposed classifier optimizes certain loss functions. Its performance is assessed on several synthetic and real benchmark data sets with good results, especially in settings where only a small fraction of the data are labeled.